Listen to this Post
Introduction:
In cybersecurity, passive reading never stops a breach—hands-on simulation does. The post shared by Mohamed Hamdi Ouardi delivers a curated arsenal of seven GitHub projects covering Splunk, Windows forensics, log analysis, Active Directory, security assessments, vulnerability management, and malware analysis, plus a new AI-driven tool called NeuroSploit. This article transforms those resources into a structured, actionable lab guide for SOC analysts, blue teamers, and IT engineers who want to build real-world defensive skills today.
Learning Objectives:
- Deploy and configure Splunk for real-time log ingestion, correlation, and alerting using both Linux and Windows event sources.
- Perform Windows forensic investigations and Active Directory attack simulation with mitigation commands.
- Set up a malware analysis sandbox and integrate AI-assisted exploitation detection using NeuroSploit.
You Should Know:
1. Splunk Power-Up: From Installation to Threat Hunting
Splunk is the industry standard for SIEM (Security Information and Event Management). The GitHub repo linked (https://lnkd.in/d2YR_wYA) provides dashboards and searches. Here’s how to set up a free Splunk Enterprise trial on Ubuntu 22.04 and ingest Windows Event Logs.
Step‑by‑step guide:
- Download Splunk: `wget -O splunk.tgz ‘https://www.splunk.com/en_us/download/splunk-enterprise.html?download’` (replace with actual link from the site)
- Extract and install: `sudo tar -xzf splunk.tgz -C /opt` → `cd /opt/splunk/bin` → `sudo ./splunk start –accept-license`
– Enable boot-start: `sudo ./splunk enable boot-start`
– On Windows, install the Universal Forwarder from Splunk’s site, then forward logs: `.\splunk add forward-server:9997 -auth admin:pass` and `.\splunk add monitor “C:\Windows\System32\winevt\Logs\Security.evtx” -index main`
– In Splunk Web (http://splunk_ip:8000), create an alert for 5 failed logins in 10 minutes: Search `index=main EventCode=4625 | stats count by user, src_ip | where count > 5` → Save as alert → Trigger a script or email.
Linux command for log forwarding (syslog): `sudo ./splunk add monitor /var/log/auth.log -index linux_auth`
This turns raw logs into actionable intelligence.
- Windows Forensics Deep Dive – Artifacts That Catch Attackers
The Windows Forensics repo (https://lnkd.in/ds6RT2Yf) focuses on RAM, registry, and prefetch analysis. Use these tools and commands to uncover persistence and lateral movement.
Step‑by‑step guide:
- Acquire memory with `WinPmem` (https://github.com/Velocidex/WinPmem): `winpmem_mini_x64.exe -o memory.raw`
– Analyze withVolatility 3: `python3 vol.py -f memory.raw windows.pslist` to see hidden processes, and `windows.cmdline` for suspicious command lines. - Extract recent registry keys (e.g., Run/RunOnce): From a live system, run PowerShell as admin:
Get-ItemProperty -Path "HKLM:\SOFTWARE\Microsoft\Windows\CurrentVersion\Run" Get-ItemProperty -Path "HKCU:\SOFTWARE\Microsoft\Windows\CurrentVersion\Run"
- For prefetch analysis: `Get-WinEvent -LogName “Microsoft-Windows-TaskScheduler/Operational” | Where-Object {$_.Message -like “wscript”}`
- Use `Autoruns` from Sysinternals to compare baseline vs. suspicious entries: `autorunsc.exe -a -c -h -s -v > autoruns.csv`
This methodology has identified real-world Qakbot and Cobalt Strike beacons.
- Log Analysis Like a Pro – Grep, AWK, and Splunk Queries
The Log Analysis repo (https://lnkd.in/dhAZvesz) teaches parsing of Apache, Syslog, and firewall logs. Combine Linux CLI with Splunk for maximum efficiency.
Step‑by‑step guide:
- Extract all failed SSH attempts from
/var/log/auth.log:
`sudo grep “Failed password” /var/log/auth.log | awk ‘{print $9, $11}’ | sort | uniq -c | sort -nr`
– Find web shell uploads from Apache access.log:
`grep “POST” /var/log/apache2/access.log | grep -E “\.php|\.asp” | awk ‘{print $1, $7}’`
– Create a Splunk query for brute-force detection:
`index=linux_auth “Failed password” | stats count by src_ip, user | where count > 20 | table src_ip, user, count`
– For Windows Security logs (Event ID 4625), use PowerShell:
`Get-WinEvent -FilterHashtable @{LogName=’Security’; ID=4625} | Select-Object -First 10 | Format-List`Pro tip: Use `jq` for JSON logs (e.g., from cloudtrail): `cat cloudtrail.json | jq ‘.Records[].eventName’ | sort | uniq -c`
- Active Directory Hardening – Attack Paths and Mitigations
The Active Directory repo (https://lnkd.in/d5uPB29b) includes tools like BloodHound and PowerView. Understand common attacks then block them.
Step‑by‑step guide for defense:
- Detect Kerberoasting (requesting TGS for service accounts with weak SPNs):
In Windows Event Viewer, look for Event ID 4769 (Ticket Granting Service) with a large number of requests from a single user.
Use PowerShell to query: `Get-WinEvent -FilterHashtable @{LogName=’Security’; ID=4769} | Where-Object {$_.Properties.Value -like "RC4"}` - Mitigate by enforcing AES encryption for service accounts: </li> </ul> <h2 style="color: yellow;">`Set-ADServiceAccount -Identity svc_sql -KerberosEncryptionType AES128,AES256`</h2> <ul> <li>Prevent LLMNR/NBT-NS poisoning: Disable via Group Policy (Computer Config → Administrative Templates → Network → DNS Client → Turn off multicast name resolution → Enabled) or PowerShell: </li> </ul> <h2 style="color: yellow;">`Set-ItemProperty -Path "HKLM:\Software\Policies\Microsoft\Windows NT\DNSClient" -Name "EnableMulticast" -Value 0`</h2> <ul> <li>Use `PingCastle` (free) to assess AD health: `.\PingCastle.exe --server YOUR_DC --healthcheck` These steps cut off common attacker pathways used in 70% of internal pentests.</li> </ul> <ol> <li>Vulnerability Management Lifecycle – From CVSS to Remediation</li> </ol> The Vulnerability Management repo (https://lnkd.in/diYtukyY) integrates OpenVAS, Nmap, and DefectDojo. Build a weekly patching workflow. <h2 style="color: yellow;">Step‑by‑step guide:</h2> <ul> <li>Install OpenVAS (Greenbone) on Ubuntu: `sudo apt update && sudo apt install gvm -y` </li> </ul> <h2 style="color: yellow;">`sudo gvm-setup` (wait for the admin password)</h2> <h2 style="color: yellow;">`sudo gvm-start`</h2> <ul> <li>Run an authenticated scan against a target Linux machine: `gvm-cli --gmp-username admin --gmp-password pass socket --socketpath /var/run/gvmd.sock --xml "<create_task><name>Linux_Scan</name><target id='...'/></create_task>"` - Use Nmap for quick service enumeration: `sudo nmap -sV --script vuln 192.168.1.0/24 -oA network_scan` - For Windows, use `Test-WSMan` to check for missing patches: `Test-WSMan -ComputerName target -Authentication Default` then compare with `Get-HotFix` output.</li> <li>Remediate critical CVSS ≥ 7.0 within 48 hours: Automate with Ansible playbooks that run `apt upgrade` or Windows Update via PSWindowsUpdate module.</li> </ul> Document everything in DefectDojo (the repo includes a docker-compose setup) to track SLAs. <ol> <li>Malware Analysis Sandbox – Static & Dynamic Deep Dive</li> </ol> The Malware Analysis repo (https://lnkd.in/dfq-u4HS) covers both static and dynamic techniques. Set up a safe, isolated environment. <h2 style="color: yellow;">Step‑by‑step guide:</h2> <ul> <li>Static analysis on Linux (without executing): `file malware.exe` → `strings malware.exe | grep -i "http\|cmd\|powershell"` </li> </ul> <h2 style="color: yellow;">`sha256sum malware.exe` → look up hash on VirusTotal.</h2> <ul> <li>Dynamic analysis using Cuckoo Sandbox (install via `cuckoo` on Ubuntu 20.04): Configure a Windows 10 VM inside VirtualBox, then run: </li> </ul> <h2 style="color: yellow;">`cuckoo submit --machine win10 malware.exe`</h2> Cuckoo generates a JSON report with API calls, network traffic, and dropped files. - For quick behavioral analysis on Windows, use `Process Monitor` (procmon) with filters: `procmon.exe /AcceptEula /BackingFile c:\logs\pmc.pml` → run malware → stop capture → use `procmon` GUI to filter by `Process Name` and `Result: ACCESS DENIED` (indicates AV bypass attempts). - Automate with FLARE VM (free Windows-based analysis environment) from FireEye: install via Chocolatey. Remember: always use an air‑gapped or non‑networked VM for dynamic analysis. <ol> <li>NeuroSploit & AI in Cybersecurity – Defensive and Offensive AI</li> </ol> NeuroSploit (linked as https://lnkd.in/dJndk_7h) appears to be an AI‑augmented exploitation framework. To defend against AI‑driven attacks, build your own detection models. <h2 style="color: yellow;">Step‑by‑step guide:</h2> <ul> <li>Install and test NeuroSploit (assuming a Python tool): `git clone https://github.com/example/neuroexploit (actual URL from the post)` </li> </ul> <h2 style="color: yellow;">`cd neuroexploit && pip install -r requirements.txt`</h2> Run a sample: `python neuro_sploit.py --target localhost:8080 --payload "reverse_shell"` <h2 style="color: yellow;">Note: Only use on systems you own.</h2> <ul> <li>To detect AI‑generated phishing payloads, train a simple classifier on email text: [bash] from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB ... load benign and malicious email datasets model.fit(tfidf, labels)
- Deploy with ELK stack: ship HTTP logs to Logstash, run a Python script that scores each request using the model, and flag high‑risk ones in Kibana.
- For Windows, use `Sysmon` Event ID 1 (process creation) and feed the command lines into a lightweight ONNX model using
WinML.
AI threats are evolving; the best defense is to build your own AI detection layer now.
What Undercode Say:
- Key Takeaway 1: Passive learning is dead – these seven GitHub repos plus NeuroSploit provide a complete lab environment that mimics real SOC, forensics, and malware analysis tasks.
- Key Takeaway 2: Automation of log analysis and vulnerability management using command-line tools (grep, awk, Splunk SPL, PowerShell) reduces mean time to detect (MTTD) from days to minutes.
- Analysis: The shift toward AI-assisted exploitation (NeuroSploit) demands that defenders adopt similar AI models for anomaly detection. However, most organizations lack the in‑house skill to train models, creating a dangerous gap. The provided hackosquad platform (mentioned in the comments) fills this by offering free, gamified blue‑team challenges. Expect to see a surge in “AI red vs. blue team” exercises in 2026.
Prediction:
Within 12–18 months, AI-driven SOC automation will replace 40% of Level‑1 alert triage, forcing analysts to upskill into AI security operations. NeuroSploit-like tools will become common in adversary simulators, leading to a new certification track (e.g., “Certified AI Security Analyst”). Cloud‑based hands-on platforms (like hackosquad) will overtake traditional video courses because they measure practical execution, not just completion. Organizations that fail to integrate the projects above into their internal training will suffer from both talent gaps and AI‑powered breaches.
▶️ Related Video (70% Match):
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: Ouardi Mohamed – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
