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Let me start with a personal opinion: I really like Petra Security’s user interface.  No offense to Microsoft Sentinel, but Petra’s UI feels modern, intuitive, and built for real-world investigation. With Microsoft, things are powerful — no doubt — but often buried in layers of menus and dashboards. Petra, on the other hand? Everything is just… right there. And that makes a big difference when you're knee-deep in incident response or hunting through user activity. ------------------------------------------------------------------------------------------------------------ 🔍 Not a Full Microsoft 365 Monitor — But the Best for What Matters Most Petra doesn’t aim to replace Microsoft Defender, Sentinel, or all your SIEM tools. It's not trying to be everything . But what it does  focus on — identity and account activity  — it does exceptionally well . Once the Petra app is approved by a Microsoft 365 admin (using OAuth), it starts collecting and analyzing the most critical logs  in your environment: Entra ID (formerly Azure AD) Exchange Online SharePoint Microsoft Teams Yes, logins are tracked — but they’re only about 2%  of the story. The real value lies in everything else. ------------------------------------------------------------------------------------------------------------ 🧑‍💼 User Intelligence: Before Logs Come In, Petra Knows the User Before we even touch logs, Petra collects rich identity information for every user: Full name and email Job title Whether the account is active or disabled Last password change Assigned Employee ID (if any) Phone number (if present) Authentication method : whether the user uses just a password, or also has MFA like Microsoft Authenticator And this part is so underrated. In Microsoft, you have to dig into separate portals or click multiple layers deep to get all this info. In Petra, it's presented in one clean view — which is super helpful during investigations . You can even quickly check which users don’t have MFA  enabled — something every security team should monitor. Because let’s be real: if users don’t have MFA set up, and your security team doesn’t catch it — it’s a problem . ------------------------------------------------------------------------------------------------------------ 🧭 The “Activity” Tab — Petra’s Unified Log View Petra doesn’t just give you logs. It gives you investigative context  in a timeline. And it calls this the Activity  panel. You can see everything here: Successful and failed login attempts File accesses Inbox actions SharePoint interactions Teams activity Everything is filterable. Let’s say you want to find all failed logins  — easy. Just filter for Incorrect password and boom, it’s there. Want to drill down on one user’s failed password attempts? Add that user email as a filter in username column and you're done. This isn’t just helpful — it’s fast. Investigators can zero in on anomalies within seconds . ------------------------------------------------------------------------------------------------------------ 📧 Exchange Logs — The Gold Standard for Email Investigation Here’s where Petra really won me over: the way it handles Exchange activity . You can see: Emails received , read , sent , and deleted Actions performed by the user Subject lines of the emails 😍 (yes, subject lines  — very helpful in investigation) Email rules created by the user Got a suspicion about a phishing email that led to compromise? Go check the subject line and delivery time. Done. Want to see if the attacker set up a malicious inbox rule? Filter for inbox rule creation  — it’s that easy. Petra even captures: Transport rules Mail sync events External sharing Delegate access Everything — in one  pane. Filters: (Few And Many more) No more switching between Microsoft 365 Security Center, Exchange Admin Center, and Sentinel. It’s all here.  That’s what I love about Petra. ------------------------------------------------------------------------------------------------------------ When it comes to Microsoft 365 investigations, we often talk about logins and email activity — but there’s so much more beneath the surface . And honestly, SharePoint and OneDrive logs  are where a lot of the real impact lives. Think about it: attackers don’t just want to log in  — they want to steal data . And where is that data? 👉 SharePoint and OneDrive. That’s why I was genuinely impressed by how Petra Security handles these logs. 🧾 Every File Interaction Captured: SharePoint & OneDrive Petra tracks everything  a user does inside SharePoint and OneDrive: ✅ File Accessed ✏️ File Modified 📥 File Downloaded 🔁 File Synced You might ask, “Why is this so important?” Well, let me walk you through a real-world scenario — especially for those newer to incident response. 🧠 Scenario: The Silent Breach An attacker gains access to an M365 account. There’s no suspicious email activity and no new inbox rules. But in SharePoint: They browse a folder named “Payment Docs” Download Invoices_Q4_2025.xlsx Sync an entire user directory to their machine Access a document called passwords.txt Now without Petra, this might go completely unnoticed — especially if you're only reviewing login logs. But Petra stitches everything together. You can filter for downloads , file syncs , and modifications . You’ll see timestamps, file names, actions taken, and the user’s IP or device. This is why SharePoint and OneDrive logs matter . Petra gives them the attention they deserve. ------------------------------------------------------------------------------------------------------------ 💬 Teams Logs: Chat, Meetings, File Sharing We won’t go too deep here, but yes — Petra also tracks Teams activity . That includes: 🧵 New chats created 📎 Links or files shared 📅 Meetings scheduled 👤 Participant joins/leaves and Many More These logs are crucial for spotting lateral movement, phishing via Teams, or even attackers trying to extract data from group chats. ------------------------------------------------------------------------------------------------------------ 🔐 Authentication Logs: Who Changed What? Petra tracks authentication method changes  across all users. So, you’ll know: When a user removed  MFA When they added  a new method (like Microsoft Authenticator or SMS) If they’re only using a password (⚠️ red flag!) Why is this important? Because often, attackers try to downgrade authentication after getting in. Seeing those changes in plain view — without digging — is a massive win for any SOC analyst. ------------------------------------------------------------------------------------------------------------ 💻 Devices, Permissions, and App Registrations Let’s talk about the remaining three log sources in Petra captured: 1. Devices Log Tracks every device tied to a user — by: Device name User ID Type (mobile/laptop/desktop) Perfect for identifying rogue endpoints or signs of lateral movement. 2. Permissions Log Want to know which users have admin rights  or custom roles ? This log shows: Role name Role description Assigned users Very helpful during privilege reviews and investigations involving privilege escalation. 3. App Registration Log Petra tracks all enterprise and personal apps  added into your M365 environment. You can see: Which apps were installed Who registered them When they were added This is where attackers sometimes try to sneak in persistence — by registering apps with elevated API access. ------------------------------------------------------------------------------------------------------------ 🚨 All of This in One View — With Context Seeing all of this in one interface, filterable by: IP User Country Device App Log type …is honestly what sets Petra apart. It’s centralized, simple, and fast . ------------------------------------------------------------------------------------------------------------ No flipping through five admin portals. No writing KQL queries. Just answers. ------------------------------------------------------------------------------------------------------------ ⚡ Coming Up: Petra's Claim of “Zero False Positives” — Real or Just Hype? Petra claims to deliver 100% zero false positives. That’s a bold statement. Next, we’ll dive into what that really means, how their machine learning model works behind the scenes, and whether it actually delivers on this promise in real-world investigations. Stay tuned. 👀 ------------------------------------------------------------------------------------------------------------ Upcoming Article : (Petra Security’s “Incidents” Tab — A Game-Changerfor M365 Breach Investigations) https://www.cyberengage.org/post/petra-security-s-incidents-tab-a-game-changer-for-m365-breach-investigations ------------------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------------------------------------ A few days ago, I left my job. Yup — packed up my virtual desk, dropped a goodbye emoji in Slack, and thought, “I’m finally free! I’ll take a break, maybe two or three weeks off. No writing, no tech, just peace.” Fast forward to today — and what the hell am I doing? Writing. Again. Like some kind of caffeine-powered content gremlin who just can’t stay away from tech blogs. ------------------------------------------------------------------------------------------------------------ Before we dive in... Huge shoutout to J  — you know who you are! I know everyone’s dying to know his full name, but let me check with the man himself before I start blowing up his phone with fame. Just know this: without J, this article wouldn’t exist, and I'd probably still be staring at a blank page. Thanks, legend. ----------------------------------------------------------------------------------------------------------- When I first came across Petra, I honestly wasn’t expecting to be this impressed. Petra is an OAuth-based security app for Microsoft 365 that does one thing — and does it incredibly well : identity threat detection .  Think of it as what Microsoft’s Entra P1/P2 should’ve been  — except smarter, more accurate, and way less expensive. ------------------------------------------------------------------------------------------------------------ 🔍 What is Petra? Petra works by ingesting Microsoft Entra ID (formerly Azure AD)  audit logs in real time. It doesn't need an agent, and it doesn't demand heavy configuration. All you do is send your client an authorization link, and once the Microsoft 365 admin approves the Petra app (with read access to audit data), Petra starts pulling the logs. That’s it. You’re up and running. No endpoint integration, no Defender licensing nightmares, no P2 tax. Just raw, real-time analysis of identity logs. And here’s the best part — it works even with the most basic Microsoft 365 license , unlike Microsoft’s native "risky users/logins" features that require a full P2 license per user . ------------------------------------------------------------------------------------------------------------- 🤯 How It Works (and Why It’s So Accurate) Petra is built by a team of mathematicians — and honestly, it shows. Instead of relying on basic rule matching or threshold-based alerts, Petra runs ML models  that evaluate 20–30 behavioral signals per user . This includes: Login geography and frequency Time-of-day access patterns Operating system and browser fingerprinting ISP profiling Travel history and anomalies And more… Whenever a new audit event is pulled, it’s passed through Petra’s behavioral models. These models are constantly learning and evolving, tailored to each environment, and shockingly precise. I’ve been in cybersecurity for years — and I don't say this lightly — Petra’s accuracy has completely changed the game for me  when it comes to identity monitoring. (From - @J) Now for me I got a chance to speak with someone, and their philosophy is clear: "Every identity has a fingerprint. You just need to look in the right places." That’s exactly what Petra does. ------------------------------------------------------------------------------------------------------------- 🔐 What About Write Access? By default, Petra is read-only. But there’s an optional write access  feature (which I’ve personally enabled) that allows Petra to: Lock user accounts Kill active sessions Cut off live threats in real-time This turns Petra from just a passive observer into a proactive response engine . And again, it's all scoped and approved via OAuth — so no messy script permissions or service accounts floating around. ------------------------------------------------------------------------------------------------------------- 🧠 Petra vs. Entra P2 Let’s be honest: Microsoft’s "Risky Users" and "Risky Logins" often feel like they were built a decade ago. Detection is slow, imprecise, and gated behind expensive licenses. Petra steps in as a modern, ML-powered alternative that: Doesn’t require P2 licensing (If you have that's awesome) Is far more accurate Offers real-time detection and optional automated remediation Works out of the box without complex integrations ------------------------------------------------------------------------------------------------------------- 🚫 Why It’s M365 Only (For Now) I asked whether Petra might expand to other ecosystems like Google Workspace, but realistically, it’s unlikely. The Entra audit logs are rich, detailed, and consistent , making them ideal for behavioral modeling. In contrast, Google’s logs lack the depth and granularity Petra depends on. (From - @J) So for now, Petra is focused on Microsoft 365 — and honestly, that’s more than enough. Because identity  remains the most exploited attack surface in enterprise environments. ------------------------------------------------------------------------------------------------------------- 💬 @J Thoughts No tool in recent memory has immediately  reduced my workload and boosted my confidence like Petra has. It’s the kind of solution I wish I had years ago. Identity-based breaches are notoriously hard to detect. But with Petra, I can honestly say: If something weird happens in your tenant — you’ll know about it. Fast. I’d love to see Petra in 100 client environments today. That’s how confident I am. Tool : - https://www.petrasecurity.com/ ------------------------------------------------------------------------------------------------------------- ✍️ Coming Up Next Article Name: (Petra Security: The UI, the Logs, and Why I Genuinely Prefer It Over Microsoft Sentinel) https://www.cyberengage.org/post/petra-security-the-ui-the-logs-and-why-i-genuinely-prefer-it-over-microsoft-sentinel If you’re running a Microsoft 365 environment and identity is your top concern — you owe it to yourself. Stay tuned. 🔐

Updated on 15 July, 2025 Understand Hayabusa completely check out below article: https://www.cyberengage.org/post/hayabusa-a-powerful-log-analysis-tool-for-forensics-and-threat-hunting Hayabusa Command Arsenal for Deep Analysis: 🖥️ 1. computer-metrics – Which Machines Logged the Most? Before you even start analyzing logs, you might want to know: Which system created the most log entries?  That’s where computer-metrics comes in. s. 🔧 Example Commands: # On a live system hayabusa.exe computer-metrics --live-analysis # On a directory of logs hayabusa.exe computer-metrics -d logs/ # On a single EVTX file hayabusa.exe computer-metrics -f system.evtx ⚠️ Heads-up: Windows sometimes logs inconsistent computer names (like lowercase vs uppercase or even a different name altogether in Win11), so use this as an estimate , not gospel truth. 📊 2. eid-metrics – Know Your Event ID Distribution Want a quick summary of what types of events (Event IDs)  dominate your log files? That’s where eid-metrics helps. It prints out the total count and percentage  of each Event ID across logs, separated by channel. 🔧 Example Commands: # On a live system hayabusa.exe eid-metrics --live-analysis # On a directory of logs hayabusa.exe eid-metrics -d logs/ # On a single file hayabusa.exe eid-metrics -f system.evtx Perfect when you're trying to spot outliers or excessive logging behavior . 📁 3. log-metrics – Get the Big Picture Think of this as your log metadata report . It gives you: Log file names Computer names Number of events First & last timestamps Channels & Providers 🔧 Example: hayabusa.exe log-metrics --live-analysis hayabusa.exe log-metrics -d logs/ This is a great way to sanity-check your input  before diving into detection or timeline work. 🔐 4. logon-summary – Who Logged In (and Failed)? This one’s a favorite in IR cases. It summarizes user logons , showing: Usernames Success counts Failure counts 🔧 Examples: # On live system hayabusa.exe logon-summary --live-analysis # On a directory of EVTX files hayabusa.exe logon-summary -d logs/ Perfect for identifying brute-force attempts , suspicious user activity , or just getting a quick login audit. 🎯 5. pivot-keywords-list – Find What’s Weird This one’s pure gold  for threat hunting. It generates a list of keywords  (like usernames, hostnames, process names, etc.) seen in logs — so you can find outliers or suspicious entities. 💡 Pro tip: Use -m critical to only look at keywords in critical alerts , and build up from there. 🔧 Examples: # View pivot keywords from critical events hayabusa.exe pivot-keywords-list -d logs/ -m critical # Save results to files hayabusa.exe pivot-keywords-list -d logs/ -m critical -o keywords or hayabusa-3.3.0-win-x64.exe pivot-keywords-list --live-analysis -m critical -o keywords --no-wizard It’ll generate files like keywords-Users.txt, keywords-IpAddresses.txt, etc. 🛠 Use case:  Take that keyword list and use it with grep to build a custom timeline: grep -f keywords.txt timeline.csv Customize the search fields by editing the config file: ./rules/config/pivot_keywords.txt 🔎 6. search – Deep-Dive with Keywords or Regex Hayabusa’s search command isn’t limited to detection results — i t lets you search across all  events , even those not flagged by rules. 🔧 Examples: # Search for 'mimikatz' in all logs hayabusa.exe search -d logs/ -k "mimikatz" # Search for multiple keywords hayabusa.exe search -d logs/ -k "mimikatz" -k "kali" # Case-insensitive search hayabusa.exe search -d logs/ -k "mimikatz" -i # Search using regex (e.g., IP addresses) hayabusa.exe search -d logs/ -r "(?:[0-9]{1,3}\.){3}[0-9]{1,3}" # Field-specific search (e.g., WorkstationName) hayabusa.exe search -d logs/ -r ".*" -F WorkstationName:"kali" 🧠 Wrap-Up: Power at Your Fingertips With these commands, Hayabusa becomes more than just a Sigma rule engine  — it turns into a full-blown, flexible DFIR toolkit . Here’s a quick recap: Command Purpose computer-metrics See log volume per system eid-metrics View Event ID distribution log-metrics Show log metadata (timestamps, channels, etc.) logon-summary Summarize login activity pivot-keywords-list Pull out high-value keywords for hunting search Deep keyword & regex searches csv-timeline / json-timeline Build visual timelines of suspicious events ---------------------------------------------------------------------------------------------------------- 👉 Use these tools together for fast, smart, and scalable threat hunting — whether you're working a single laptop or an enterprise breach. -----------------------------------------------------Dean---------------------------------------------

Updated on July 15, 2025 By someone who hates dry cybersecurity guides as much as you do Let’s talk about a seriously underrated threat-hunting combo: Hayabusa  and Sigma rules . If you're into threat detection, blue teaming, or incident response — or even if you're just curious about how to spot evil from Windows logs — this is one rabbit hole you'll actually  enjoy going down. --------------------------------------------------------------------------------------------------------- 🤔 First off, what even is  Sigma? Alright, let’s simplify. Think of Sigma  as the "universal translator"  for security logs . It was created by Thomas Patzke  and has grown into a massive open-source project supported by the community. Unlike tools like Snort (for network stuff) or YARA (for file-based threats), Sigma deals with log data  — like Windows Event Logs, syslogs, cloud logs, etc. Here's the beauty: Sigma gives us a platform-agnostic  way to describe suspicious behavior. That means you can write a detection rule once and use it across different SIEMs or tools. It’s kind of like writing one email and having it auto-translated for everyone in your office, no matter what language they speak. Handy, right? --------------------------------------------------------------------------------------------------------- 💻 Enter Hayabusa: The Samurai of Windows Log Hunting Now, what if I told you there’s a tool that reads Windows event logs and automatically applies Sigma rules to hunt for threats ? Say hello to Hayabusa  — which literally means “falcon”  in Japanese. 🦅 And just like a falcon, this tool is fast, sharp, and built for one thing: spotting evil in your event logs . Created by Yamato Security, Hayabusa  can churn through EVTX files or even JSON-converted logs and flag anomalies based on a growing rule set. 📦 What does Hayabusa support? Runs on Windows, macOS, and Linux Accepts: Local system event logs Saved .evtx files Full directories of logs Outputs: CSV  (for spreadsheet nerds) HTML  (for a pretty summary) JSON  (for API nerds or automation fans) 🔍 Why Should You Care? Because logs don’t lie  — but they do  hide things really well. Windows logs are full of juicy forensic breadcrumbs: logon events, privilege use, command executions, service creations, and more. The problem is, they’re overwhelming. You’ll drown in logs before you spot the one that matters. Hayabusa + Sigma is like having a log-sniffing dog that doesn’t get tired. 🧠 Quick Tip: Keeping Hayabusa Updated Threats evolve fast. So should your detections. With the simple command: C:\Users\Akash's\Downloads\hayabusa-3.3.0-all-platforms> .\hayabusa-3.3.0-win-x64.exe update-rules Hayabusa fetches the latest Sigma rules  from the official repo and merges them into its detection engine . It’s like giving your detection engine a brain upgrade on the fly. ⚡ Real Use Case: CSV-Timeline (Output) Let’s say you want to run Hayabusa on your own machine. You just do: C:\Users\Akash's\Downloads\hayabusa-3.3.0-win-x64-live-response>hayabusa-3.3.0-win-x64.exe csv-timeline -l -o output1.csv Output: ⚡ Real Use Case: HTML Report Let’s say you want to run Hayabusa on your own machine and create HTML Report. You just do: C:\Users\Akash's\Downloads\hayabusa-3.3.0-win-x64-live-response>hayabusa-3.3.0-win-x64.exe csv-timeline -l -o result.csv -H output.html Boom. You get an HTML summary with clickable links showing which Sigma rule matched and why. ⚠️ One caveat though: That HTML report is a summary. For the nitty-gritty details , like which process or user triggered the alert, you’ll want to check the CSV output . That’s where the real breadcrumbs are. 📚 Bonus: What Makes Sigma So Awesome? Over 3000 rules  (and counting!) for all types of threats Can describe behaviors across: Windows Linux macOS Cloud platforms Apps and more Easy to write, easy to read (even for beginners) Growing ecosystem of tools that support it (not just Hayabusa) --------------------------------------------------------------------------------------------------------- 🔧 Pro Tip: Combine with Velociraptor If you're managing multiple endpoints, try plugging Hayabusa into Velociraptor. It’s an open-source digital forensics and incident response (DFIR) framework, and Hayabusa fits in beautifully to give you log-based detection across your fleet. Check out My Velociraptor series Link below: https://www.cyberengage.org/courses-1/mastering-velociraptor%3A-a-comprehensive-guide-to-incident-response-and-digital-forensics --------------------------------------------------------------------------------------------------------- Imagine this: you’ve got 50 GB of event logs , and you’re tasked with figuring out what happened, when it happened, and where it happened . Doing that manually? Forget it. You’ll be buried in logs till next week. That’s where Hayabusa’s timeline mode  steps in. With a simple command, Hayabusa can: Parse  a folder full of EVTX files (yes, even 50+ GB of them) Apply Sigma rules  to detect threats Generate a CSV timeline  showing you what went down and when That CSV file becomes your investigative cheat sheet. --------------------------------------------------------------------------------------------------------- 🧪 Real-World Example: Hunting Across Logs with a Timeline Here's the full command we used on a windows system with a big ol’ folder of logs: hayabusa csv-timeline -d eventlogs/ -T -o hayabusa-threathunting.csv -E --timeline-start "2025-07-01 00:00:00 +00:00" --timeline-end "2025-07-15 00:00:00 +00:00" --no-color or .\hayabusa-3.3.0-win-x64.exe csv-timeline --live-analysis -T -o hayabusa-threathunting.csv -E --timeline-start "2025-07-01 00:00:00 +00:00" --timeline-end "2025-07-15 00:00:00 +00:00" --no-color Let’s break that down: Argument What it does -d eventlogs/ or --live-analysis for live Directory containing EVTX files -T Enables timeline output in the terminal -o hayabusa-threathunting.csv Where to save the CSV file -E Only review specific event IDs (speeds things up) --timeline-start / --timeline-end Analyze logs only within a specific time range --no-color Removes terminal color codes for clean output Pretty neat, right? --------------------------------------------------------------------------------------------------------- 📊 Why CSV Output Is a Game Changer Hayabusa's CSV output  includes super useful fields like: Timestamps Event IDs Threat severity Rule titles MITRE ATT&CK IDs (if available) Computer name (if analyzing multiple systems) That last part is huge  for environments with more than one system. You can correlate threats across endpoints and spot patterns like lateral movement or domain-wide compromise. --------------------------------------------------------------------------------------------------------- 🧰 Organizing the Madness with Timeline Explorer Now you’ve got this CSV — what next? Sure, you can open it in Excel or Google Sheets, but if you really want to pivot, filter, and sort like a DFIR wizard , use Timeline Explorer by Eric Zimmerman . Here’s what you do: Open the CSV in Timeline Explorer Drag-n-drop columns like: Level Rule Title Computer Now you can group alerts by severity , then drill down by rule, then system. Boom. Instant clarity. --------------------------------------------------------------------------------------------------------- 📦 Don’t Stop at CSVs – Integrate & Automate Hayabusa doesn’t lock you into CSVs. You can also: Use json-timeline for structured JSON output Load results into SIEM platforms Push into Elasticsearch  for dashboards Integrate with Neo4j Desktop  for graph-based attack path analysis You can also change Hayabusa’s output format by using custom profiles: hayabusa-3.3.0-win-x64.exe list-profiles This shows you all the output templates. Want to include ATT&CK IDs or remove some columns? Create your own custom YAML profile. --------------------------------------------------------------------------------------------------------- ⚙️ Wait, How Do I Get Logs From All My Machines? Great question. Grab logs from remote systems using a quick PowerShell helper script:📥 Copy-RemoteWindowsLogs.ps1 This lets you collect EVTX files across your domain and organize them by hostname, ready for Hayabusa to chew through. --------------------------------------------------------------------------------------------------------- 🧩 Other Hayabusa Tricks You Should Know Besides csv-timeline, Hayabusa comes packed with other commands: update-rules – grab the latest Sigma + Hayabusa rules from GitHub json-timeline – same timeline, just in JSON search – keyword-based hunting across logs logon-summary – view logon patterns metrics – get event frequency stats --------------------------------------------------------------------------------------------------------- 🔐 New in Hayabusa v2.18.0+: Live Response Packages! Hayabusa now offers special Live Response packages  designed for endpoint use. These packages include the binary, an XOR-encoded Sigma rules file, and a single config file — all bundled together. Why? To avoid triggering antivirus tools like Windows Defender and to minimize file writes  on disk (protecting forensic artifacts like the USN Journal ). Just look for the ZIP files with live-response in the name. --------------------------------------------------------------------------------------------------------- Final Thoughts If you’re working in threat detection, response, or forensics, you don’t want to sleep on Hayabusa . It’s fast. It’s flexible. It supports the Sigma rule ecosystem. And most importantly — it makes sense of the chaotic mess that is Windows Event Logs. So next time you’re looking at a pile of .evtx files wondering where to even start… just remember: Hayabusa + Sigma = Instant Timeline, Actionable Threats. Give it a shot — your future self will thank you. 🙌 -------------------------------------------Dean------------------------------------------------------------- Check Out below article where i have shared few commands to get you started with analysis: https://www.cyberengage.org/post/hayabusa-exe-essential-commands-for-in-depth-log-analysis

Memory acquisition has emerged as a transformative development in the field of digital forensics. While it has been in practice for over 15 years, recent advancements in tools and techniques have made it an essential component of forensic investigations. Yet, despite its significance, misconceptions and outdated practices still hinder its widespread adoption. What is Memory Acquisition? Memory acquisition involves capturing volatile data, which includes information stored in RAM (Random Access Memory) and other ephemeral data such as active network connections, running processes, and system state. Volatile data is crucial because it is lost when a computer is powered off, making it a perishable yet invaluable source of evidence. Breaking Down the Myths Historically, the practice of pulling the plug on a powered-on system dominated forensic approaches. This method, while simple, results in the loss of volatile data, leaving investigators with limited evidence. Critics of memory acquisition often argue that it alters the evidence, making it inadmissible in court. However, this belief is outdated. Modern courts and organizations, including the U.S. Department of Justice, emphasize the importance of documenting and preserving volatile data . ****Failing to collect this information can now be viewed as evidence destruction******, especially when such data could refute claims like the "Trojan defense" or "SODDI" (Some Other Dude Did It). Why Memory Acquisition is Critical 1. Combatting Encryption Challenges The growing prevalence of encryption tools like BitLocker, PGP, and TrueCrypt has heightened the importance of memory acquisition. Pulling the plug on an encrypted system can render evidence inaccessible, as encryption keys and other critical data are often stored in RAM while the system is running. Memory acquisition allows investigators to capture these keys and access encrypted information. 2. Preserving Valuable Evidence Volatile data includes crucial details such as: Current network connections Active processes and running applications Residual data from exited processes Passwords in plaintext These pieces of evidence are instrumental in reconstructing activities on a system, identifying malicious actions, and refuting or supporting claims of remote control or malware involvement. Best Practices for Memory Acquisition 1. Document Everything Investigators must meticulously record their actions, including the tools used, timestamps, and any changes made during the process. Proper documentation ensures the integrity and admissibility of the evidence. 2. Use Trusted Tools Modern memory acquisition tools like WinPMEM , and encryption detection tools like Magnet Forensics Encrypted Disk Detector, and Elcomsoft Disk Decryptor are equipped to handle the complexities of contemporary systems . These tools are designed to operate on both 32-bit and 64-bit systems, including Windows 11, and comply with security requirements like digital driver signing. 3. Prioritize Live Response The standard practice is to capture volatile data before shutting down a system . Conducting on-site triage helps identify critical evidence and ensures that data is preserved in its most useful state. In cases involving encryption, capturing data while the system is operational is paramount. 4. Leverage System Artifacts Operating systems often create artifacts like hibernation files (hiberfil.sys) , crash dumps (memory.dmp) , and page files (pagefile.sys or swapfile.sys) . These files can provide partial or complete snapshots of RAM and serve as valuable sources of memory data for analysis. Memory Analysis and Advanced Techniques Memory analysis tools such as Volatility and MemProcFS offer advanced capabilities to examine captured data. These tools enable investigators to: Analyze process space and network connections Detect advanced malware techniques like code injection and rootkits Recover encryption keys, chat logs, internet history, and more Memory Analysis with Volatility 3, Memproc5, Strings, and Bstrings! 🎉 Using these tools, I’ve created a detailed blog covering all of them. Check out the link below if you’re interested in learning memory analysis. Happy exploring! 🚀 https://www.cyberengage.org/courses-1/mastering-memory-forensics%3A-in-depth-analysis-with-volatility-and-advanced-tools Detection of encryption Forensic experts can also utilize commercial tools like EDD and Elcomsoft Disk Decryptor to determine w hether drives are encrypted before acquiring memory . This step is crucial because if the drives are encrypted, obtaining the encryption key—either by asking the client or through memory acquisition—becomes essential. As for tool Exploring Magnet Encrypted Disk Detector (EDDv310) I have already created article do check it out Link below: https://www.cyberengage.org/post/exploring-magnet-encrypted-disk-detector-eddv310 For tool Elcomsoft Disk Decryptor There’s an article by Oleg Afonin that you can check out here: https://blog.elcomsoft.com/2020/07/live-system-analysis-discovering-encrypted-disk-volumes/ What I particularly like about Elcomsoft Disk Decryptor i s its ability to indicate whether it’s safe to shut down the computer . Based on this information you can further decide what additional information should be collected to support the analysis. The Future of Memory Acquisition As encryption adoption continues to rise, memory acquisition will become a standard practice in forensic investigations. Emerging technologies like Modern Standby in Windows 10 and 11 increase the likelihood of finding hibernation files, further enhancing the ability to capture volatile data . Investigators must adapt to these changes and embrace memory acquisition as a critical step in their workflows. Conclusion Memory acquisition is no longer a complex or optional task—i t is a necessity in modern digital forensics. By prioritizing the collection of volatile data and leveraging the latest tools and techniques, investigators can preserve critical evidence, overcome encryption challenges, and strengthen the integrity of their cases. That’s all for today! See you in the next article. Take care! 😊 (Dean)

Jump Lists have undergone significant changes in Windows 10 and 11 , just like LNK shell items . These changes have expanded the range of recorded data, making Jump Lists even more valuable for forensic analysis . While some changes may seem subtle, they provide deeper insights into user activities. ----------------------------------------------------------------------------------------------------- 1. Quick Access and Its Role in Jump Lists What is Quick Access? Quick Access is a Windows File Explorer feature introduced in Windows 10  that allows users to quickly find recently opened files and folders . It also lets users pin frequently used items for easy access. How is Quick Access stored? Quick Access data is saved in a dedicated Jump List . This is usually one of the largest Jump Lists  on a system because it records multiple file types and locations. It provides a broad view  of recently accessed items. Limitations: Quick Access does not always record every opened file . S ome files may not have LNK (shortcut) information  in this lis t. 💡 Best Practice:   Since Quick Access doesn’t capture everything, a lways cross-reference it with application-specific Jump Lists   (e.g., Jump Lists from Microsoft Word, Adobe Reader, or other software). ----------------------------------------------------------------------------------------------------- 2. Tracking Newly Created Files vs. Opened Files Windows 10 and 11 have changed how files appear in Jump Lists  when they are created or saved to a different location (e.g., using "Save As"). Key Differences: Previously :  Jump Lists mainly recorded files that were opened. Now:   Jump Lists also capture files when they are newly created  in a different location. How to Determine If a File Was Created or Just Opened: When a file is newly created , it appears in both Quick Access  and the dedicated application Jump List  (but not for all file types). You can compare timestamps : If the **** target creation timestamp   matches the DestList last modified timestamp** , the file was likely newly created . If the timestamps do not  match , the file was simply opened   rather than created. ----------------------------------------------------------------------------------------------------- 3. Tracking Folder Copying with Jump Lists One of the most important updates  in Windows 10 & 11 is that Jump Lists now track folder copying . What Does This Mean? When a user copies a folder , Windows creates an entry in the File Explorer Jump List . This applies to both single and multiple folder copies . Mounted drives  and external storage devices  are also tracked. Why Is This Important? If a user copies a folder to a USB drive , Windows records: The copied folder's name The destination location The time the folder was copied  (based on the target creation timestamp ) 💡 Forensic Insight:   Just like file creation tracking, matching the target creation timestamp  with the DestList last modified timestamp  helps determine if the folder was simply opened or copied to another location. ----------------------------------------------------------------------------------------------------- 4. Tracking Taskbar Search Activity in Microsoft Edge Another notable update is that J ump Lists now record searches made from the Windows taskbar —specifically in Microsoft Edge’s Jump List . How Does This Work? When a user performs a search in the taskbar and clicks the "Best Match" result , the search is recorded. These entries reference "Microsoft.Windows.Cortana" , linking them to taskbar search results. The URL parameters  in the entry contain the searched term . The entry’s last modified time  logs the exact time the search was performed. 💡 Forensic Tip:   By analyzing Jump Lists, investigators can see what the user searched for and when —even if browser history has been deleted! ----------------------------------------------------------------------------------------------------- Final Thoughts Jump Lists in Windows 10 and 11 offer more data  than ever before, making them a powerful forensic artifact . These changes allow us to track: ✅ Recently accessed files and folders ✅ Files created or saved to a different location ✅ Copied folders, including external USB drives ✅ User search activity in Windows Taskbar and Microsoft Edge To get the most accurate results , always cross-check Jump Lists with other forensic artifacts like LNK files, event logs, and shell bags . By doing so, you can build a clearer picture  of user activity on a system. Stay tuned for more deep dives into Windows forensic artifacts! 🚀 --------------------------------------------------Dean------------------------------------------

Note to My Readers: I apologize for not being very active on the website or posting new articles over the past few weeks. I've been dealing with some personal matters that have required my attention. I appreciate your patience and understanding during this time. I’ll be back to writing and updating the site as soon as things settle down. Thank you for your continued support. Windows, developed by Microsoft, has been a cornerstone of personal and professional computing since its debut in 1985. As of March 2022, Windows holds a dominant global market share of 75.7% , making it the most widely used operating system worldwide. Among these installations, 74.82% run Windows 10, while 8.45% have transitioned to Windows 11. Microsoft reports that over 1.4 billion devices globally are running either Windows 10 or 11 (Microsoft, 2022a). Microsoft plans to support at least one version of Windows 10 until October 14, 2025 . As the end of Windows 10 support nears, the adoption of Windows 11 is expected to rise significantly . This shift underscores the importance for digital forensic examiners to understand the differences and similarities between these two operating systems, especially in terms of investigative artifacts and security features. There is a great article written by Andrew Rathbun: Covering entire sharing link you can check it out https://www.sans.org/white-papers/windows-10-vs-windows-11-what-has-changed/ Forensic Artifacts This section reviews whether key artifacts from Windows 10 persist in Windows 11 and highlights any forensic differences. Below is a detailed analysis of prominent artifacts. LNK Files and Jump Lists The Shell Link (.LNK) Binary File Format underwent revisions in June 2021, but no significant forensic changes were identified. Similarly, Jump Lists, which are collections of . LNK files associated with applications, remain unchanged between Windows 10 and 11. $Recycle_Bin Metadata Files Metadata files within the Recycle Bin ($I30) show no observable differences between Windows 10 and 11 . Amcache The Amcache artifact is identical in both Windows 10 and 11 . Registry Hives Registry hives in Windows 11 exhibit significant changes, with over 35,000 added or removed Keys and Values compared to Windows 10 . While these changes currently lack forensic significance, ongoing research is essential given the volume of modifications. The Registry hives affected were the following: BCD-Template COMPONENTS DEFAULT DRIVERS ELAM NTUSER.dat SAM SECURITY SOFTWARE SYSTEM UsrClass.dat Windows Timeline The Windows Timeline feature, introduced in Windows 10, was removed in Windows 11 However, its database, ActivitiesCache.db, still exists in Windows 11 . Prefetch No differences were found in the Prefetch (.pf) files between Windows 10 and 11. Event Logs Comparative analysis revealed that Windows 11 introduced new Event Providers and updated or removed others compared to Windows 10 . Shellbags Shellbags, which track folder navigation, operate identically in Windows 10 and 11. Folder creation and navigation yielded identical results in both systems. Windows Search Index (.ESE) Database The Windows Search Index artifact (Windows.edb) retains its structure but exhibits notable differences in Windows 11 . The SystemIndex_PropertyStore table in Windows 11 has an additional column, System_Setting_SettingsEnvironmentID, and a table number change from #17 (Windows 10) to #15 (Windows 11). Additionally, Windows 11’s ESE engine version (9400) differs from Windows 10’s (9180), which affects database repair compatibility. Web Browsers Edge Chromium 101.0.1210.53 produced identical artifacts on both Windows 10 and 11. ShimCache (AppCompatCache) ShimCache functions similarly in Windows 10 and 11 . SQLite Databases Windows 10 and 11 share many SQLite databases, commonly found in browser artifacts and system files. Research indicates these databases remain consistent between the two versions . Directory Listings A GitHub repository, https://github.com/AndrewRathbun/VanillaWindowsReference , offers directory listings for various Windows versions. A comparison between Windows 10 and 11 reveals differences in file and folder structures, useful for forensic research. Security Features in Windows 11 Trusted Platform Module 2.0 is mandatory for Windows 11, ensuring hardware-based security for all devices. Windows 11 supports secure, passwordless access through TPM 2.0, reducing credential theft risks with multifactor authentication. Hypervisor-Protected Code Integrity (HVCI) :- Enabled by default on new installations, this f eature uses virtualization to enhance memory integrity and protect against exploits. Transport Layer Security 1.3 is the default , improving encryption protocols and reducing handshake times. T LS 1.2 is supported as a fallback . DNS Over HTTPS :- This protocol encrypts DNS queries , protecting against attackers who monitor or redirect traffic. SMB Protocol Enhancements :- Updates include AES-256 encryption, SMB over QUIC for untrusted networks, and accelerated signing for improved file service security. Enhanced Wi-Fi security with WPA3 and Opportunistic Wireless Encryption ensures better protection on public networks. Conclusion While Windows 11 shares many similarities with Windows 10 , its security upgrades and new features present opportunities and challenges for DFIR professionals . Ongoing research will be vital as Microsoft delivers yearly updates, introducing potential new artifacts and forensic considerations. Do not forget to check out the article written by Andrew Rathbun. Link mentioned above. Take care for now, See ya in next article --------------------------------------------------Dean----------------------------------------

You know how Google knows what you were thinking before you even typed it? That’s not magic—it’s analytics . Google Analytics and marketing tools like HubSpot leave behind tracking cookies  on devices, and guess what?  These aren’t just marketing gold—they're digital breadcrumbs  that we, as forensic investigators, can use to understand a user’s activity. Let’s break this down like we’re sitting together at a DFIR roundtable. So, What Are These Cookies and Why Do We Care? Google Analytics sets a bunch of cookies that track a user’s interaction with a website. While this helps advertisers figure out where users are coming from and what they do on the site, it also helps us  in incident response and digital forensics. The main players in Google’s tracking cookie lineup are: __utma __utmb __utmz (And a few others like utmc, utmt... but let’s keep our eye on the forensic prize.) These cookies are part of what used to be called the Urchin Tracking Module  (UTM)—a tech acquired by Google back in 2005. Dissecting the __utma Cookie This one’s a long-liver —with a 2-year expiration date—and super valuable for us . It tells a detailed story about the user's visits  to a site. Here’s the format: __utma=..... Example: __utma = 57409013.9999999999.1600000000.1700000000.1710000000.10 Translation: 57409013: Domain hash (keep it the same if on same domain) 9999999999: New unique user ID (any random long number) 1600000000: First visit (timestamp for ~2020) 1700000000: Previous visit (timestamp for ~2023) 1710000000: Current visit (timestamp for ~2024) 10: Now it looks like this user has visited 10 times Why this matters: This gives us a timeline  for a user across visits and helps identify repeat behavior. Just keep in mind— different browsers, private mode, or cookie clearing  resets this data. So multiple values can exist for the same human. Meet __utmb: The Session Timer This one’s short-lived—just 30 minutes ! It’s all about tracking sessions . __utmb=... Example: __utmb = 57409013.1.10.1720000000 If a user clicks a phishing link, for example, and it triggers some malicious activity, this cookie might help us zero in on when  that session started. Meet__utmz: The User’s Path Think of this one as the referral detective . It lasts 6 months and shows how the user landed  on the site. __utmz=.... Example: __utmz=57409013.1349969023.3.2.utmcsr=rss1.0mainlinkanon|utmccn=... or __utmz=57409013.1746076800.4.3.utmcsr=google|utmccn=(organic)|utmcmd=organic|utmctr=buy%20headphones|utmcct=/ This can tell us if they came from 57409013  = same domain hash 1746076800  = timestamp for May 1, 2025 4  = this is the user's 4th visit 3  = their 3rd different traffic source utmcsr=google  = source: Google utmccn=(organic)  = campaign: organic search utmcmd=organic  = medium: organic (vs. referral or direct) utmctr=buy headphones  = search keyword utmcct=/  = landed on homepage Why it’s useful: If you’re investigating malware that was delivered via a malvertising campaign  or a specific site, this helps reconstruct the user's path . ------------------------------------------------------------------------------------------------------------ Beyond Google: HubSpot Cookies Are Forensic Gold Too Alright, so not every site uses Google Analytics. S ome go with tools like HubSpot , especially in marketing-heavy environments. The key HubSpot cookies: __hstc hubspotutk hsfirstvisit Meet __hstc: HubSpot's Main Tracker This one sticks around for 2 years  and tracks repeat visits: __hstc=..... Example: __hstc=104275039.abc1234567890abcdef9876543210abcd.1704067200000.1743465600000.1748649600000.5 You’ve got: Part Value Meaning Domain Hash 104275039 A numeric identifier for your domain, hashed internally by HubSpot. Visitor ID abc1234567890abcdef9876543210abcd A unique ID for the visitor. Looks like an MD5 hash. This is used to identify return visits from the same browser/device. First Visit Timestamp 1704067200000 This is in Unix milliseconds  → corresponds to Jan 1, 2024 . Marks the first time  this user visited the site. Previous Visit Timestamp 1743465600000 This corresponds to April 1, 2025 . Marks the second-most-recent  visit. Current Visit Timestamp 1748649600000 This corresponds to May 31, 2025 . Marks the current visit . Visit Count 5 This is the 5th time  the visitor has come to the site.   Forensics win: These values give us insight into visit behavior across time, just like Google Analytics, but from a different provider —which might not be blocked or deleted as often. hubspotutk: The Long-Lived Fingerprinter This one is wild—it’s valid for 10 years . Even though its internal structure isn’t documented, this unique value can help us correlate activities  across visits and sessions. If we find the same hubspotutk in different cookies across different websites, we may be able to link activity  to the same user device. hsfirstvisit: First Contact Also has a 10-year expiration. It shows: How the user got to the site on their first visit A long UNIX-style timestamp (just chop off the last 3 digits to convert) Example: $ date -u -d @1672574400000 date -u -d "2023-01-01 12:00:00" +%s  This might tie the user’s first visit to a job posting or email link—even if the page is no longer online. ------------------------------------------------------------------------------------------------------------ Why This Matters in Investigations These tracking cookies can: Help build timelines  of activity Correlate a device/user across domains Identify the entry point  in phishing or exploit delivery Highlight repeat behavior  or anomalous browsing But remember: They’re browser- and session-specific Private mode or cookie clearing wipes them Different browsers = different cookie stores So always combine with browser history , cache , web artifacts , and tools like: Plaso/log2timeline Browser History Capturer KAPE with browser modules ------------------------------------------------------------------------------------------------------------ Wrapping Up Tracking cookies like utma, utmz, and __hstc are often overlooked in forensic investigations. But when interpreted correctly, they provide valuable context  that complements log files and system artifacts. So next time you're staring at a blob of cookie data, take a closer look—it might just lead you to a breakthrough in your case. -----------------------------------------Dean-----------------------------------------------

--------------------------------------------------------------------------------------------------- Thanks for all the support on the Wireshark article! https://www.cyberengage.org/post/master-wireshark-tool-like-a-pro-the-ultimate-packet-analysis-guide-for-real-world-analysts I know there are already tons of articles out there on HTTP—but trust me, this one’s different. Give it a read, and you’ll see exactly what I mean. --------------------------------------------------------------------------------------------------- Hey folks Today, let’s take a walk through a protocol that all of us use literally every day —HTTP. Yup, HyperText Transfer Protocol . Even if you’re not a hardcore networking nerd, if you've ever opened a webpage (which, hello, you're doing now!), you’ve used HTTP. But if you're into digital forensics, incident response , or just cybersecurity in general, knowing how HTTP works isn't just a bonus—it’s critical . And trust me, there's a lot more to it than just "the thing that gives me web pages." ------------------------------------------------------------------------------------------------------------ First Things First: What Is HTTP? HTTP is a plaintext protocol , which means it’s readable. You and I can literally look at a packet of HTTP data and figure out what’s going on without needing fancy tools. It’s also stateless , meaning each request doesn’t remember the one before it. Every request stands on its own. This might sound weird at first—like, how does your web browser remember where you left off? That’s where cookies , sessions , and tokens  come in (topics for another day 😄). ------------------------------------------------------------------------------------------------------------ Why Should a Forensic Investigator or Incident Responder Care? I’m glad you asked 😎 Whether you're investigating a rogue employee, a full-blown APT, or just checking someone’s shady web browsing, HTTP is going to show up a lot . In fact, you’ll probably run into HTTP traffic in almost every case . Now, here’s the twist: with the rise of full-disk encryption , incognito modes , and BYOD  (bring-your-own-device) policies, disk artifacts aren’t always enough . That’s where network data  comes in. If you’ve got packet captures (PCAPs)  available, you can: Reconstruct entire web sessions Pull down files that were downloaded (think: malware EXEs or phishing pages) Track API calls to remote services Monitor machine-to-machine activity (bots, implants, or automated tools) Detect C2 traffic  (command & control) And that’s not just theory. I’ve worked with many malware analysts who help us dissect C2 channels running over HTTP. Even if the attacker encrypted the payload, the URLs, headers, or timing patterns can still tell you a lot. ------------------------------------------------------------------------------------------------------------ Real-Life Use Case: Web Server Compromise Let's say a web server gets popped. Sure, you’ll look at logs and disk evidence. But what if the attacker cleared logs or used living-off-the-land  techniques? That’s when HTTP traffic analysis becomes your best friend. By reviewing actual network traffic , you might catch: File uploads via POST Command injections Suspicious API usage Attacker beacons to external servers ------------------------------------------------------------------------------------------------------------ HTTP Versions – It’s Not All 1.1! Okay, here’s a little version history in plain English: HTTP/1.0  – Old-school. One request per connection. HTTP/1.1  – Still widely used. Keeps connections alive. This is what you’ll see most in PCAPs. HTTP/2  – Multiplexed. Multiple requests over one connection. Super common now. HTTP/3  – The future. Built on QUIC  (based on UDP), not TCP. Crazy fast. Still being adopted. According to W3Techs (as of now), HTTP/2  is used by over 50% of websites, and HTTP/3  is slowly gaining ground (~10% but growing fast). ------------------------------------------------------------------------------------------------------------ Dissecting an HTTP Request – Let’s Get Nerdy for a Second Here’s a simple GET request: GET /time/1/current?cup2key=9:wz8PuwCb6IQ1sPJTx92bCpndCnsugtTLkdpVppulvZE&cup2hreq=e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 HTTP/1.1\r\n Host: clients2.google.com This line breaks down into: GET  – Request method cup2key=9:wz8PuwCb6IQ1sPJTx92bCpndCnsugtTLkdpVppulvZE&cup2hreq=e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 – The URI (Uniform Resource Identifier) (Request Strings) HTTP/1.1  – Protocol version Then you’ve got headers (like Host, User-Agent, Accept, etc.) Fun fact: GET and POST are the most common methods. GET is used to fetch  data. POST is used to send  data (like login credentials, form data, or file uploads). Here's a quick cheat sheet of other methods: Method What It Does HEAD Like GET, but fetches only headers (no body) PUT Uploads a file or resource DELETE Deletes a resource OPTIONS Asks what methods the server supports TRACE Echoes back the request (used for debugging) CONNECT Used to create a tunnel, often for HTTPS Some of these, like TRACE and CONNECT, are often blocked by firewalls or disabled on servers because of their potential abuse. ------------------------------------------------------------------------------------------------------------ Forensic Tips & Bonus Nuggets HTTP requests can contain query strings  (?name=value&foo=bar), which might hold sensitive search terms, login attempts, or injection payloads. Headers like User-Agent, Referer, and Cookie can reveal browser behavior , session IDs, and possible spoofing. When malware uses HTTP as a C2 channel, it often mimics legitimate browser behavior  to blend in. Look for anomalies! Some HTTP-based malware also abuses API endpoints , like /api/upload, /checkin, or /status. These are usually dead giveaways in custom C2 protocols. One Last Thing... Not all HTTP traffic is visible today. With HTTPS  (the secure version), a lot of the content is encrypted. But don’t worry— the domain (SNI), headers, and timing  can still tell you a lot, especially if you're using TLS interception (in legal environments, of course). ------------------------------------------------------------------------------------------------------------ let’s casually break down something that often looks boring but is super powerful when you're into digital forensics, incident response, or even threat hunting— HTTP Request Headers . What’s the Scene? Imagine someone visited metadrive.io . When they did that, their browser quietly made an HTTP request to metadrive.io. What’s interesting is how  their browser told the website about itself—and that's where headers come in. Let’s start with the raw request: GET / HTTP/1.1\r\n Host: metadrive.io\r\n Connection: keep-alive\r\n Upgrade-Insecure-Requests: 1\r\n User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/136.0.0.0 Safari/537.36\r\n Accept: text/html, application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/ *; q=0.8,application/signed-exchange;v=b3;q=0.7\r\n Accept-Encoding: gzip, deflate\r\n Accept-Language: en-US, en; q=0.9\r\n r\n ------------------------------------------------------------------------------------------------------------ Okay, deep breath! Host Header – The MVP of HTTP/1.1 Host: metadrive.io\r\n Why it matters: In HTTP/1.1, the Host header is required . Without it, the server won’t know which website you want—especially important when one server hosts multiple sites. Think of it as the “to:” address on a letter. ------------------------------------------------------------------------------------------------------------ User-Agent – Browser's ID Card (Well, Sort Of) User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/136.0.0.0 Safari/537.36\r\n What it tells us: This is your browser bragging about who it is. In this case: Browser identified as Chrome 136 on Windows 10 (64-bit) Now here's the kicker: This value is completely customizable . Anyone can spoof it. You and I can literally install browser extensions like User-Agent Switcher  and pretend to be Googlebot, Internet Explorer from 2001, or even a toaster (okay, maybe not—but close!). ------------------------------------------------------------------------------------------------------------ Accept Headers – What the Client Wants Accept: text/html, application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/ *; q=0.8,application/signed- Accept-Encoding: gzip, deflate\r\n Accept-Language: en-US, en; q=0.9\r\n These are pretty straightforward. Accept:  What content types the browser can handle (HTML, XML, etc.) Accept-Language:  Tells the server the user's preferred languages. Useful for geo-profiling. Accept-Encoding:  Whether the browser can handle compressed responses like gzip. Also, note the q value—it shows preference. For instance, q=0.9 means “I like XML, but not as much as plain HTML.” ------------------------------------------------------------------------------------------------------------ Cookies – The Trail of Breadcrumbs (In this example its not there but adding so it will be eays for you) Cookie: prov=...; hubspotutk=...; docs_hero=x; hero=none prov=... – Likely a session or user identification token hubspotutk=... – A HubSpot tracking cookie used for analytics and form submissions docs_hero=x – Possibly a custom flag to track a docs page UI state hero=none – Another UI state flag or feature toggle Cookies are little pieces of data stored by your browser from websites. They're often used to maintain state —which is important because HTTP itself is stateless . Without cookies, every click would feel like starting from scratch. Types of cookies: Session Cookies:  Gone when the browser closes. Persistent Cookies:  Stick around until they expire (or you delete them). For us forensic folks, cookies can reveal: Logins Tracking IDs User behavior across sessions You’d be surprised how much we can correlate just from cookie IDs. ------------------------------------------------------------------------------------------------------------ Authorization – Base64 and Secrets Authorization: Basic Example: Authorization: Basic bmV3dXNlcjpzM2NyM3RwYXNz Here’s where you might find credentials. This is Basic Auth  and it’s basically (pun intended) the base64 encoding of username:password. So bmV3dXNlcjpzM2NyM3RwYXNz decodes to newuser:s3cr3tpass Modern sites mostly use token-based auth or OAuth, but for internal apps or older services, you still  find Basic Auth. When found, it’s gold for an attacker or an investigator. ------------------------------------------------------------------------------------------------------------ X-Forwarded-For – Tracing Real IPs (Kinda) X-Forwarded-For: , If a request passes through proxies, this header might show the original client IP . BUT , it’s easily spoofed. An attacker can just add their own X-Forwarded-For and pretend to come from anywhere (say, an internal IP like 192.168.1.11). Some servers trust this blindly— not good . That’s why this header is a common target in IP-based bypasses . ------------------------------------------------------------------------------------------------------------ Proxy-Authorization – Auth to Use the Proxy Proxy-Authorization: Basic bmV3dXNlcjpzM2NyM3RwYXNz Like Authorization, but used when a client needs to authenticate to a proxy  server. Again, base64—same risks apply. ------------------------------------------------------------------------------------------------------------ Referer (Yeah, It’s Misspelled) – Where You Came From Referer: https://www.cyberengage.org/search?q=forensic This tells the server which page you clicked from. Handy for: Analytics (e.g., “what drove traffic here?”) Security (e.g., detecting CSRF or phishing flows) Investigation (e.g., mapping user navigation paths) Here’s the cool part: if you’re moving from HTTPS → HTTP , browsers are supposed  to suppress or truncate this header. But in practice, some browsers still leak enough info to tell where you came from. ------------------------------------------------------------------------------------------------------------ Other Fun Headers Upgrade-Insecure-Requests: 1 → Tells the server “hey, if you support HTTPS, switch me there.” Cache-Control: max-age=0 → Basically says: “Please don’t serve me a cached page; I want it fresh.” ------------------------------------------------------------------------------------------------------------ Dissecting an HTTP Response– Let’s Get Nerdy for a Second So far, we’ve talked a lot about HTTP requests — what the client sends to the server. But now it’s time to flip the script. Let’s talk about what the server sends back  in response. Let’s Start from the Top — Status Line Here’s a classic example: HTTP/1.1 200 OK This single line tells you three key things : Protocol Version : HTTP/1.1 — this should match the client’s request version. Status Code : 200 — tells you if the request went okay or something broke. Status Text : OK — human-readable, but the client doesn’t really  care what this says. It could say "Success", "All Good", or even "Nice Try Buddy" 😄 — as long as the number is 200, the meaning is the same. 💡 Common Status Codes You Should  Know Let me list a few real-world ones we bump into all the time: Code Meaning 100 Continue – Client can keep sending request body 200 OK – Everything’s good 301 Moved Permanently – Resource has a new home 302 Found – Temporary redirect 304 Not Modified – Client’s cached copy is still good 400 Bad Request – Syntax error from client 401 Unauthorized – Need authentication 403 Forbidden – You don’t have permission 404 Not Found – Resource doesn’t exist 407 Proxy Auth Required – You need to auth via proxy 500 Internal Server Error – Oops, something’s broken 503 Service Unavailable – Overload or maintenance 511 Network Auth Required – Seen in public Wi-Fi portals For threat hunters : Seeing lots of 400s from the same IP? That might be scanning/recon. A sudden switch from 500s to 200s during POST requests? Could be SQL injection , where the server backend choked on bad input before the attacker got it right. 🔍 Real Response Header Breakdown Here’s a full sample response: accept-ranges: bytes\r\n content-disposition: attachment\r\n content-length: 1963\r\n content-security-policy: default-src 'none'\r\n server: Google-Edge-Cache\r\n x-content-type-options: nosniff\r\n x-frame-options: SAMEORIGIN\r\n x-xss-protection: 0\r\n x-request-id: c1349dbe-bb51-41bc-a142-e4ba95d94a1c\r\n date: Sat, 24 May 2025 04:26:33 GMT\r\n age: 38934\r\n last-modified: Sat, 24 May 2025 04:24:20 GMT\r\n etag: "45281ea"\r\n content-type: application/octet-stream\r\n alt-svc: h3=":443"; ma=2592000, h3-29=":443"; ma=2592000\r\n cache-control: public,max-age=86400\r\n coprocessor-response: download-server\r\n \r\n Now let’s decode it like detectives 🕵️: Cache-Control, Expires, and ETag These tell you how caching should work. Cache-Control: private — Only the user’s browser should cache it, not shared proxies. ( if u see Cache-Control: public which means: The response is cacheable by any cache  — both the user’s browser  and shared caches  ) Expires:  — When the cache is no longer valid. or max-age=86400 (It remains fresh and reusable for 1 day) ETag: "" — Unique fingerprint for the content; helps compare if content changed. Great for web performance  and forensic timeline building . Content-Type and Content-Encoding Tells you what kind of content and how it’s packed: Content-Type: text/html; charset=utf-8  — HTML page in UTF-8 encoding. or content-type: application/octet-stream\r\n =tells the browser (or any client) that the server is sending raw binary data . Content-Encoding: gzip — It's compressed, so your client needs to decompress. Content-Length Size of the actual data (after decompressing, if needed). content-length: 1963: — 1963 bytes. X-Frame-Options: SAMEORIGIN Mitigates clickjacking by saying: “Only I can frame myself!” Date Exact time the response was generated. Useful when reconstructing timelines or tracking malware behavior. date: Sat, 24 May 2025 04:26:33 GMT  Investigator Tip: If your endpoint says it made the request at 1:52 PM, but the server's timestamp says it responded at 1:47 PM — you might have clock skew  on the client. This can seriously mess with your timeline , so cross-check time sources always. Fun fact: Some malware variants use this Date: header as a seed value for their DGA (Domain Generation Algorithm)  — clever, huh? Connection: keep-alive (if found) With HTTP/1.1 , one of the cool upgrades was allowing persistent connections  — so your browser could reuse the same TCP session for multiple requests. This reduces overhead and speeds things up. The client tells the server it supports this using:Connection: Keep-Alive If the server agrees, it responds with:Connection: Keep-Alive But if either side wants to close the connection : Connection: close Investigator Tip: If you're monitoring traffic and notice lots of "Connection: close" lines mid-session, it might indicate non-browser activity — like malware making single-use requests. ------------------------------------------------------------------------------------------------------------ What About Redirects? Redirections are handled via a combination of: 300-series status codes  (like 301, 302) A Location: header that says: "Hey, go here instead!" These redirects can be abused too. Malware campaigns use redirect chains to mask the origin of malicious content. Forensics tip: Don’t stop at the first hop! ------------------------------------------------------------------------------------------------------------ Pro Tip: Watch Out for X- Headers Both clients and servers can use custom headers that begin with X-. These can carry unique identifiers , debug info , or even tracking tokens . Example: X-Request-Guid: This might help correlate a single session across multiple logs. ------------------------------------------------------------------------------------------------------------ HTTP Headers in Investigations Let’s talk real-world usage. How do these headers help during an actual incident? 1. Pastebin & Data Exfil Attackers often use public paste sites like Pastebin or SendSpace. Some malware is coded to automatically upload exfiltrated data using these services’ APIs. If an attacker has RDP or VNC access, they might just open a browser and manually do it — but the network traffic (HTTP POST requests, User-Agent headers, and API URIs) will still leave footprints. 2. User-Agent Fingerprinting If you're in a corporate environment, there’s probably a known set of legitimate User-Agent strings. Anything else? Could be: Malware Unauthorized browser Portable or dev tools Sometimes, malware adds its own version string  in the User-Agent, helping investigators quickly fingerprint infections across the environment . 3. Credential Sniffing in HTTP Basic Auth We touched on this earlier, but just a reminder — Basic Auth  sends credentials like this: Authorization: Basic bmV3dXNlcjpzM2NyM3RwYXNz That Base64 string? It’s just user:password. If you’re capturing traffic, you can extract credentials directly. 4. URI Analysis Every URI tells a story. It could be: Web searches Form submissions API calls Malware callbacks Pairing URI analysis with malware analysis gives you powerful insight into what the attacker was trying to do — exfiltrate data, move laterally, connect to command-and-control, or worse. 5. When the Disk Fails, the Network Tells All Modern attackers are smart: They use private browsing They run portable apps from USBs They clean up after themselves So maybe there’s no trace left on the disk . But network traffic? That’s harder to erase. If you have PCAPs or proxy logs, you’ve still got a shot. ------------------------------------------------------------------------------------------------------------ Final Thoughts HTTP headers might seem boring on the surface, but when you dig in — they’re loaded with useful info. From persistent connections to User-Agent strings to caching behavior and time syncing — every bit tells you something. Hope this post made it easier to see headers not as noise, but as gold dust  for a forensic investigator. -------------------------------------------------------Dean-------------------------------------------

By Dean and Co-founder(Keeping him hidden) N ote: The following is a real-world scenario. While specific details have been redacted for confidentiality, the events, risks, and discussions are authentic and reflect how quickly routine security assumptions can be challenged. ------------------------------------------------------------------------------------------------------------- It was a quiet Friday afternoon when the security team at < Redacted> received a cryptic message that disrupted the stillness: "Just letting you know, I’m traveling to [Redacted: High-Risk Country] for a personal emergency. I have my work laptop with me, but it’s off. I won’t be working remotely. I’ll be back in a few days." No warning. No travel notice. No security protocol followed. The sender was a mid-level employee—someone with access to sensitive communication channels, confidential project documentation, and internal corporate emails. She had simply vanished off the radar with a company-owned device, now located in one of the most surveilled and cyber-hostile environments  on Earth. When Silence Isn’t Golden As the message trickled up the chain of command, tension rippled through the team. The endpoint hadn’t checked in. The MDM system showed it as silent . Meanwhile, her personal phone , likely still logged into apps like Slack and Gmail, was live—connected to unknown, unmanaged, and potentially compromised networks. The war room lit up.Discussions intensified. The air was heavy with the weight of unknowns . That’s when the Manager , a cybersecurity veteran, finally spoke up—measured and calm and stated. "Hi @Co-founder," "Should we burn it all down?" Experience Speaks Co-founder leaned back in his chair, gaze steady. “Unless you suspect she’s actively cooperating with the [foreign] government, I don’t think you need to go nuclear. If FileVault is enabled and she confirms that the laptop never left her possession, we have some room for measured response.” His suggestion? Don’t jump to full device wipe—yet.Instead, perform deep threat hunting  when the laptop returns. Maybe even plant deception tokens  to monitor post-return behavior. But then, his tone shifted. And the room fell silent. “I’ve seen this before. A national from [REDACTED] traveled back home. He was coerced. Pressured. When he returned, credentials started behaving strangely. It turned out, the government had leaned on him to gain access to his employer’s network.But that was a high-profile case—the company had crossed a geopolitical red line.” When to Go Nuclear The Co-founder then delivered a dose of hard-earned wisdom: “Governments don’t waste zero-days lightly. A full-disk encryption bypass? That’s a weapon-grade exploit. If the device wasn’t seized or out of her hands, I’d avoid assuming the worst.” However, he outlined a clear response matrix: If customs had taken  the device, even briefly?→ Immediate wipe. No debate. If there’s no evidence of tampering  and the device remained in her possession?→ “Wipe sessions. Reset MFA. Change passwords. Hunt hard.” If you suspect cooperation or physical compromise ?→ “Wipe everything. Treat it like a breach.” The Measured Middle Ground His conclusion struck a balance between paranoia and practicality: “I wouldn’t make this the standard response to all international travel. But this? This is how I’d handle it. If wiping the device won’t cause operational disruption, then sure—wipe it. Better safe than sorry.” The team sat in silence again, eyes fixed on the last known signal from the laptop—thousands of miles away. Powered off… or so she said. Is Still Days Away And so the countdown begins.An employee returns soon.But what she’s really bringing back? That’s the question no one can yet answer. A trusted colleague? A compromised asset? Or a sleeper breach waiting to unfold? Stay vigilant. Because sometimes, the quietest events… hide the loudest risks.

Forensic Mindset article let’s be honest—when you're knee-deep in a digital forensic investigation or a threat hunting session, one of the biggest challenges is simply knowing where to start . Sometimes you’re lucky. You get a nice clean lead: a suspicious IP, a malware hash, or a user who clicked something shady. But more often than not, someone just comes over and drops the classic: “Something’s off… we don’t know what, but can you check it out?” Frustrating, right? But this is actually where the real DFIR (Digital Forensics and Incident Response) journey begins. ------------------------------------------------------------------------------------------------------------- The Investigative Compass: Ask the Right Questions Here’s what helps me frame my approach—and you might find this helpful too: 1. What was taken? When? Where did it go? How? Who? Classic damage assessment. This is what most stakeholders care about. What data was stolen? When did it happen? Is it still happening? "who did it" isn’t always the most urgent priority . 2. What happened just before and after the incident? Events don’t happen in a vacuum. Context is king. A login from a foreign IP five minutes before the ransomware hit? That matters. Random account creation after an attachment was opened? That’s a clue. Sometimes the thing you’re investigating is just the tip of the iceberg . Looking at the surrounding activity is how you find the rest of it. 3. How did the malware get in? Was it a phishing email? A drive-by download from a shady ad network? A vulnerable web server? You’ll often find these answers in your network logs or proxy data . Knowing how the threat entered helps you close that door and stop the same thing from happening again. 4. What else was happening on the network? This is about scoping. Are there other compromised systems? Are there lateral movements? This is where real hunting begins. A good rule of thumb: if one system is infected, chances are it’s not alone . ------------------------------------------------------------------------------------------------------------- The Most Common Entry Point: Phishing (Yeah, Still) Let’s walk through an all-too-familiar story: User connects to the corporate Wi-Fi. Logs into their domain account. Opens Outlook. Sees an email — looks legit. Clicks a link. Boom. Game over. Here’s what actually  happens under the hood: DNS request to the phishing domain. Website serves a drive-by download. User unknowingly runs the payload. It fetches a second-stage malware. Tries connecting to primary C2 — blocked. Falls back to backup C2 — success. This tiny click becomes a pivot point  for an entire compromise. Knowing the order of operations helps you know exactly what to look for in logs and network traffic. ------------------------------------------------------------------------------------------------------------- Packet Captures (pcaps): Goldmine or Nightmare? If you’ve worked with network data, you’ve seen .pcap files. These are generated by tools like tcpdump, Wireshark, or npcap (for Windows). But let’s get real—just having a pcap isn’t enough. You’ve got to ask: What interface did the capture come from? Was it a WLAN in managed mode  (data frames only)? Or monitor mode  (more detailed 802.11 frames)? Knowing how and where the pcap was captured  can save you hours of chasing false leads. Also, pcaps are heavy. On high-bandwidth networks, they can get out of hand quickly—moving them, parsing them, even opening them can be painful. ------------------------------------------------------------------------------------------------------------- NetFlow & IPFIX: Metadata Magic If you can’t get full packet captures (because... storage), the next best thing is NetFlow  or IPFIX . These are like traffic summaries  — you won’t see payloads, but you will  see what talked to what, when, and how much. Cisco started it. IPFIX is the open standard. Collectors store the data, analysts query it. It’s best used for large networks where full captures are impractical. For example: if you see 1000 connections from a IOT to an outside IP on port 443... yeah, something’s weird. ------------------------------------------------------------------------------------------------------------- Logs: Trust, but Verify Logs are amazing , but only if you: Hash them the moment you collect them. Store originals in read-only storage. Work only on trimmed-down copies. Label edits and don’t overwrite the originals. Also, retention matters. Sometimes breaches stay hidden for months . If you’re only keeping logs for 30 days, that’s not good enough. A good practice? Match your log retention to your threat landscape. At least a year for critical servers. ------------------------------------------------------------------------------------------------------------- Scoping: What Else Was Happening? After finding malware or a breach, don’t stop there. Ask: Were other systems affected? Is this lateral movement? Is the malware beaconing out? This process — scoping  — is crucial . Think of it like a crime scene investigation: don’t just look at the body, look at the entire room. ------------------------------------------------------------------------------------------------------------- let’s slow down and talk about something we often take for granted: how we actually get  the traffic in the first place . We usually get excited about libpcap, tcpdump, and Suricata rules (yes, guilty here 🙋‍♂️), but without the right hardware setup, those tools are like a car without wheels. First Stop: The Humble Switch (And Port Mirroring) Let’s start with the network switch . These little workhorses make sure devices talk to the right destinations by segmenting traffic. Great for performance — but bad for traffic visibility. On a switched network, we can’t just plug into a random port and expect to see all the traffic. Switches are too smart for that. So this is were port mirroring to the rescue!  Also known as SPAN ports  (Switch Port Analyzer). Here’s how it works: The switch duplicates traffic from one or more ports (or even VLANs). It sends that duplicate stream to a specific port you designate. You plug your capture box or sensor into that mirrored port, and boom — you're now watching the action. Why it’s awesome: Already built into most enterprise switches. Zero hardware cost (just configuration). No need to interrupt the network. But there’s a catch: The mirrored port might choke if you throw too much data at it. Even if the switch supports 24 ports at 1Gbps, your SPAN port is still just one 1Gbps link. If traffic exceeds what the mirror port can handle, it can drop packets — or worse, the switch might disable the mirror completely. ------------------------------------------------------------------------------------------------------------- 🛠️ Enter the Network TAP: Built for One Job, and It Nails It When port mirroring isn’t cutting it — we turn to the network TAP  . These are hardware devices designed solely to duplicate network traffic. No bells, no whistles — just glorious packets. Different types of TAPs: Basic TAPs Split the traffic into two directions (ingress and egress). You’ll need to reassemble them (called aggregation) using software or another device. Aggregation TAPs Combine both directions into one full-duplex stream — super handy for monitoring from a single interface. Regenerating TAPs They clone traffic to multiple output ports , so you can feed data to multiple sensors or analysis tools at the same time. This is gold during IR when one team might be doing full packet capture while another is looking at behavior or writing detections. ------------------------------------------------------------------------------------------------------------- Cloud’s Not Left Out Either Guess what? Traffic mirroring isn’t just for on-prem anymore. Cloud vendors finally gave us what we need: AWS : Has VPC Traffic Mirroring , which mirrors traffic from ENIs (Elastic Network Interfaces) to a collector. Google Cloud : Offers Packet Mirroring , which works across instances in a VPC. These are awesome for cloud visibility, but remember to monitor your costs — mirroring traffic can rack up bandwidth charges! TAPs vs. Port Mirroring: What You Really Need to Know Feature Port Mirroring (SPAN) Network TAP Cost ✅ Free (built-in) ❌ Expensive Reliability ⚠️ Can drop packets ✅ Rock solid Setup Impact ✅ No downtime ❌ Needs brief downtime Complexity ✅ Simple config ⚠️ Varies with features Use Case 🟡 Light monitoring 🟢 Heavy-duty, IR, forensics ------------------------------------------------------------------------------------------------------------- Let's talk about Network Flow Data . Yup, we’re talking NetFlow, VPC Flow Logs, DNS logging, and all the juicy network breadcrumbs attackers leave behind. Whether you’re responding to a breach or threat hunting proactively, this kind of telemetry is pure gold . What the heck is NetFlow and why should I care? NetFlow is basically metadata about traffic that moves across a network. It won’t show you full packet content (so don’t expect to see passwords or payloads), but i t tells you who talked to who , for how long, how many packets, and how much data . Think of it like your phone bill: you may not hear the convo, but you know who called who, for how long, and from where. ----------------------------------------------------------------------------------------------------------- Where Can You Get Flow Data From? Internal Devices Most routers and firewalls (especially enterprise-grade ones) can export flow logs . Many switches with Layer 3 or 4 capabilities can do this too. Just note: it's often disabled by default , so check that setting first. Want endpoint-level logging? You can configure workstations and servers using tools like: fprobe pmacct nprobe Now, if you’re in the middle of an incident or running a hunting operation, you can even pair these tools with port mirroring   to collect flows tactically. Super useful if you want focused visibility without touching every endpoint. ----------------------------------------------------------------------------------------------------------- Cloud Platforms For those running workloads in the cloud: AWS  gives you VPC Flow Logs Azure  has NSG Flow Logs Google Cloud  provides VPC Flow Logs These are cloud-native equivalents of NetFlow and can be integrated right into your detection pipeline. Just remember to tune what’s being logged  so you’re not overwhelmed. ----------------------------------------------------------------------------------------------------------- Why Is NetFlow So Useful in IR? Let’s say you detect a suspicious IP today. You can go back and ask: Has this IP connected to us before? What systems did it talk to? Was data sent out (exfiltration)? Is there a pattern of beaconing (C2)? The cool thing is NetFlow data is super fast to query , and because it’s metadata, it's not as sensitive or heavy  as full packet capture. That means storage and privacy concerns are much lower. You can also spot odd behavior like: Massive outbound flows → data theft? Repetitive small bursts of traffic → C2? Connections to known bad IPs → APT action? ----------------------------------------------------------------------------------------------------------- Okay, but how do we actually  get the logs? Great question. Just because the devices can  generate logs doesn’t mean you’ll have access. Many security appliances have painful UI-based exports —especially if they’re managed by an MSSP. You must  test the log collection/export mechanism before an incident happens.   Can’t access the device? Then make sure the admins can , and fast. Or, better yet, automate  the process if possible. If logs are being sent to you from someone else make sure: The logs are secure (in transit and at rest) File formats and sizes are supported Everyone involved knows how to collect and send the data Pro tip: Set up regular drills  with your team so that collecting and reviewing logs becomes second nature. ------------------------------------------------------------------------------------------------------------- External Evidence: Often Forgotten, But Invaluable This one’s often overlooked. Your ISP Yep, your ISP may collect NetFlow from boundary routers . If you have a good relationship with them (and legal clearance), they might be able to give you insight into every bit of inbound and outbound traffic . This can be life-saving  if your internal logs were wiped or weren’t enabled. Other Organizations If your infrastructure is used to attack someone else (e.g., you got pwned and became a launchpad), they might send you logs showing: Source IP Port used Packet metadata But let’s be real— no one is going to start these conversations in the middle of a crisis.  This is where ISACs   or threat intel sharing groups  can help. Set up those channels before  you need them. ------------------------------------------------------------------------------------------------------------ Planning Your Logging Strategy There are three types  of planning scenarios: 1. Strategic/Architectural (Baked-in) This is where security folks are part of the network design  from the beginning . You decide where to place proxies, IDS sensors, and flow log exporters before  any incident happens. Pros: Zero downtime when an incident hits Continuous visibility Forces network engineers to build security into the design Cons: Expensive Requires justification (hard to show value until things go wrong) Might involve proprietary data formats (vendor lock-in) 2. Tactical/Ad-hoc Platforms This is my personal favorite—especially if you’re low on budget. Build or buy portable packet capture boxes that you can deploy anywhere  in the network when needed. Pros: Super flexible You control the setup Easier to train your team on Cons: Might need downtime to insert the box You’ll need solid documentation  for how/when/where to deploy These are best when paired with a few pre-positioned sensors in high-value areas. 3. “We’ll Figure It Out When It Happens” No. Just... no. Seriously, don’t wing it. You’ll be halfway through the breach trying to order a capture card off Amazon. Instead, build a hybrid  model: Place permanent monitors at your perimeter and crown jewels Keep a few tactical boxes on standby Train your team regularly to know how to use both ----------------------------------------------------------------------------------------------------------- Don't Forget DNS Visibility One last but crucial thing: DNS logs . DNS is everywhere, and attackers love it for C2, exfiltration, and even domain generation algorithms (DGA). Make sure: Internal DNS resolvers are logging queries and responses External DNS providers (like Google, Cloudflare, etc.) are integrated into your SIEM if they allow it DNS visibility = quicker scoping, faster identification of malware domains, and understanding attacker behavior. ----------------------------------------------------------------------------------------------------------- Wrapping It Up This stuff isn't just for blue teams. Red teamers, threat hunters, and IR folks all benefit from proper flow visibility. If you're serious about incident response or DFIR, flow data isn't a luxury— it's a necessity . Let’s keep pushing for opensource cybersecurity knowledge. --------------------------------------------Dean---------------------------------------------------------

Thanks for stopping by! I know you’ve probably come across tons of Wireshark articles already, but trust me—this one’s different. I’ve kept it real, practical, and straight from an investigator perspective. Give it a read, and you’ll see exactly what I mean.  🦈 ----------------------------------------------------------------------------------------------------------- Hey folks 👋 Dean here! So, if you’re diving into packet analysis or network forensics, you will spend a LOT of time inside Wireshark — that’s just a fact . This article is all about getting comfortable with Wireshark’s GUI and some key features that make your job easier and your analysis sharper. Wireshark’s Interface – Know Your Panes When you open Wireshark, the interface is split into three main sections  (panes), and each plays a unique role: 1. Packet List Pane This is your bird’s-eye view. Each row is a packet. By default, you'll see columns like: Time  – Time since the start of the capture (can be changed to UTC/human-readable format). Source/Destination IPs Protocol Info  – A quick summary, which is super helpful when scanning for suspicious behavior. You can fully customize this view: add, remove, or reorder columns. For instance, if you want to add http.user_agent , you can do that – empty for non-HTTP packets, of course. 2. Packet Details Pane Now we get to the fun part — protocol decoding! Here you’ll see the packet broken down by layers: Ethernet → IP → TCP/UDP → Application layer data Each section is clickable and expandable. This pane is gold  when you're analyzing weird or unfamiliar protocols because Wireshark does the heavy lifting and shows human-readable names and field values. 3. Packet Bytes Pane This is your raw hex + ASCII view. It may feel intimidating at first, but trust me — this view is powerful. You can click on a byte here and it will highlight the corresponding field in the Packet Details pane (and vice versa). ----------------------------------------------------------------------------------------------------------- Smart Display Filters – Your Best Friend in Big PCAPs When you’re staring at thousands of packets, Display Filters  are a lifesaver. You’ll find the Display Filter Toolbar  at the top. You can type stuff like: http ip.addr == 192.168.1.1 tcp.port == 443 These don’t just make your life easier — they help you zoom into  what's important without the noise. Down at the bottom, the Status Bar  tells you: Total packets Packets matching your current filter Field names and byte sizes for whatever you’re hovering over Super useful when building precise filters or understanding payload size. Layout Customization – Looks Can Boost Productivity Wireshark lets you change the pane layout! 🧱 This might sound cosmetic, but if you’re working on a small screen, a widescreen monitor, or presenting on a projector, tweaking the layout can hugely  improve usability. Choose from six layouts and decide what each pane shows. Try it. It helps. Let’s Talk OPSEC – DNS Lookups and What NOT to Do By default , Wireshark disables DNS lookups. That’s not a bug, that’s a feature. Here’s why: Performing live DNS lookups for every IP in a capture slows everything down . Worse: If you're analyzing malware traffic, querying attacker infrastructure can tip them off that you’re onto them . Never turn on  “Use an external network name resolver” if you care about stealth. Trust me — adversaries do monitor their DNS logs. Instead, use: “ Use captured DNS packet data for address resolution ”This resolves hostnames from the DNS packets in the capture — no external traffic. Timestamp Formats – Pick What Works for You Wireshark gives multiple time format options: Seconds since capture start (default) UTC (recommended) Local time With or without microsecond precision You can change this from the View > Time Display Format menu. ⚠️ Keep in mind:T imestamps in the packet metadata  (in the pcap) are in UTC. But any timestamps inside  the packet data (like HTTP headers or app logs) can be in any  timezone. So don’t mix them up. ----------------------------------------------------------------------------------------------------------- 🔧 More Tips for Better Investigations Add your own custom columns! Need to see dns.qry.name in the top view? You can add it. Use color rules to highlight suspicious traffic. E.g., make HTTP POSTs Green. Save your profiles – layout, filters, colors – for different use cases (malware, exfil, RDP analysis, etc.) HOW TO CREATE A PROFILE IN WIRESHARK Open Wireshark Go to Edit > Configuration Profiles Click New  and give it a name (e.g., MalwareAnalysis) Select it, then click OK This activates the profile—you’ll now be customizing this one Lets give you an example: 1. MALWARE ANALYSIS PROFILE Columns to Add frame.number ip.src ip.dst tcp.stream http.request.method http.host http.request.uri dns.qry.name tcp.len Coloring Rules Filter Description BG Color http.request.method == "POST" Suspicious POSTs Red dns.qry.name contains ".xyz" TOR Domain Purple tcp.port == 4444 C2 Comms Dark Red udp contains "powershell" Obfuscated Payloads Orange Output: Note: If I receive any requests, I’ll publish a follow-up article sharing a few ready-made profiles that can assist in deeper analysis. However, if you're only interested in learning how to create a profile, the information in this article should be sufficient. ----------------------------------------------------------------------------------------------------------- Lets talk about Display Filters Imagine we’re staring at a huge PCAP file — like, thousands of packets — and we need to find just the suspicious HTTP request or that DNS reply pointing to a shady domain. Manually clicking through each packet? Nope. That’s where Wireshark Display Filters  come in. And trust me, once you get the hang of them, they’ll become your best friend in traffic analysis. First, What Are Display Filters? Wireshark is already super powerful with its decoders and GUI, but display filters make it even better. Basically, display filters  allow you to tell Wireshark: "Hey, show me only the packets that match this very specific condition." And here’s the kicker — any field Wireshark can decode can also be filtered . Whether it’s an IP address, port number, DNS name, cookie value, or even the number of DNS answers — i f it’s in the packet and Wireshark can read it, you can filter it. Display Filter vs Capture Filter (BPF) You may be wondering — "Wait, isn’t that what capture filters do?" Great question. 🟢 Capture Filters (BPF)  work before  the packets are even captured — they sit close to the kernel, fast and efficient. 🟡 Display Filters  work after  the capture — they analyze the decoded traffic and give you crazy granularity. So if you're capturing live and only want HTTP port 80 traffic: tcp port 80 (That’s a capture filter , BPF-style.) But once the PCAP is saved, and you want to find HTTP packets with the word "hack" in them , that’s when display filters shine: http contains "hack" Real-Life Examples You’ll Actually Use Let’s look at some cool and practical filters  you’ll definitely end up using: 1. Find non-standard HTTP traffic containing specific text (not tcp.port == 80 and not tcp.port == 8080) and http contains "hack" 2. DNS replies with more than 5 answers (maybe shady as hell) dns.flags.response == 1 and dns.count.answers > 5 and dns.qry.name contains "drive.io" 3. Case-insensitive matching (thanks to RegEx!) http.cookie matches "(?i)dean" 4. OR multiple status codes like a pro http.response.code in {200 301 302 404} Finding Field Names for Filters One of the most common questions is: "How do I even know what field name to use?" Here’s the trick: Right-click any field in the Packet Details pane  (middle pane), and you’ll see: ✅ Apply as Filter  — applies the filter instantly 📝 Prepare a Filter  — lets you tweak the filter before running it Let’s say you’ve got a DNS query for www.cyberengage.org , and you want to filter only those. Wireshark shows the field name as: dns.qry.name == "www.cyberengage.org" That’s it. GUI does half the work for you! The Stoplight System (Green, Yellow, Red) Wireshark helps with syntax too. You’ll see the filter bar change colors: 🟩 Green  – valid syntax, ready to go. 🟥 Red  – error in field name or syntax (like missing quotes). 🟨 Yellow  – valid syntax, but potential logical issues  (like using != in the wrong way) Let’s Talk About != Here’s where things get tricky — t he != operator might not behave like you’d expect . Say you write: dns.a != 192.168.1.1 Wireshark may still include  packets with that IP. Why? Because that same packet also had a different dns.a value. So the better filter would be: dns.a && !(dns.a == 192.168.1.1) This means: " Show me packets that have any dns.a field, but NOT if any of those are 192.168.1.1." Tricky, right? But powerful once you get it. ----------------------------------------------------------------------------------------------------------- Lets talk about TCP Stream What Is “Follow TCP Stream” and Why It’s Gold Let’s start with the basics. Imagine you’re deep inside a pcap file, and you're tracking an IP address that might be talking to a shady server . You click on a suspicious packet, right-click it, and boom — there’s this magical option: “Follow → TCP Stream.” What this does is extract the entire conversation (client-to-server and server-to-client) into a single readable view. It’s color-coded too: 🔴 Red  = client → server 🔵 Blue  = server → client It’s super helpful, especially for ASCII-based protocols like HTTP, FTP, SMTP, or even Telnet . You can literally read full login attempts, commands, and responses like reading a chat transcript. A Pro Tip There’s a dropdown labeled “Entire Conversation.”   You can use that to focus on just one side of the traffic — really helpful when the streams get noisy or tangled. Not Just TCP Wireshark lets you follow UDP , TLS , and HTTP  streams too. This is great for protocols that don’t rely on TCP’s session-based structure. Beyond the Basics: Must-Know Features in Wireshark Wireshark is a beast  — no other way to put it. Decode As Alternate Protocol Sometimes threat actors do shady things like running HTTP traffic over random ports (say, port 9999). Wireshark might misinterpret the protocol. With Decode As, you can force Wireshark to analyze the traffic using the protocol you know is actually being used. Traffic Capture Tips Wireshark can both capture and analyze traffic. But beware — the GUI adds processing overhead , and you might drop packets if you’re capturing high-volume traffic. ----------------------------------------------------------------------------------------------------------- Final Thoughts In real-world cases, these tools help you go from “what’s going on?”  to “here’s exactly what happened” —faster and with more confidence. Don’t be afraid to explore, experiment, and get your hands dirty. The more comfortable you get with these tools, the more powerful and efficient your analysis becomes. Keep digging, keep questioning, and most importantly, keep learning . You’re not just reading packets—you’re uncovering stories hidden deep in the network. Thanks again for reading. I hope this walkthrough gave you something valuable—something practical you can carry into your next case or lab session. Happy hunting! ( https://wiki.wireshark.org/ ) ----------------------------------------Dean--------------------------------------------