Omar Babba
FR

Work · 16 documented projects

Built to learn. Tested to defend.

A worked governance dossier, detection pipelines, forensic and malware investigations, adversary emulation and security audits. Every project ships with its architecture, the evidence, and the lessons from getting it to work.

Clause checklist of an ISO 27001 management system showing 20 of 21 checks met
Governance, risk & compliance01

Kontrol — a worked ISMS dossier

A governance platform I built, run on a complete case: the ISO/IEC 27001 management system of a fictional water utility. 20 of 21 clauses met, 15 risks assessed, and what is not finished left visible.

ISO/IEC 27001ISO/IEC 27005ISO/IEC 42001TypeScriptPostgreSQL
SocForge lab architecture diagram with attacker, firewall, targets and SOC tools
End-to-end SOC lab03

SocForge

Twelve virtual machines on one PC, from detection to response. One attack followed end to end: attacker blocked at the firewall 24 seconds after the alert, host isolated, hunt launched, case closed — every step with its evidence.

WazuhShuffleTheHiveMISPVelociraptorOPNsense
Splunk Cloud showing the endpoint index created for log ingestion
SIEM detection07

Splunk SIEM Detection Lab

A Splunk Cloud lab documenting Windows log ingestion and Sysmon forwarding, with detection searches for brute-force and PowerShell misuse written and tested end-to-end.

Splunk CloudSysmonPowerShellWindows logs
Purple team08

APT-Simulator

A lab-safe emulation range that replays real adversary playbooks so you can answer the only question that matters: would we actually have caught this?

PythonMITRE ATT&CKJSONLDocker
Blockchain security audit09

MediChain-plus

A dual-chain pharmaceutical platform I built and then audited — 19 findings identified and remediated, including a reentrancy flaw, with security scanning wired into CI.

SolidityHyperledger FabricSlitherSemgrepGitleaks
Wazuh alert detail for a C2 beaconing detection, rule 100103
AI-augmented SOC10

PFA-SOC-IA

Built during my 2026 SOC internship at Amendis: a local Gemma2 9B model triages real Wazuh detections and hands off to TheHive, Cortex, and MISP — 100% MITRE match vs. 40% for the SIEM baseline alone, with severity routing kept strictly off the LLM's own output.

WazuhShuffleGemma2 9BTheHiveMISP
SOC dashboard showing successful exploitation against the vulnerable gateway mode
Web app security11

Web App Testing Playground

A dual-mode hardening gateway that runs the same OWASP Top 10 exploits against a vulnerable and a hardened path of one app — plus a self-discovered drive-by localhost RCE and a stateful SIEM correlation engine.

OWASP Top 10OWASP ZAPNode.jsDocker
Wazuh MITRE ATT&CK dashboard showing per-agent tactic breakdown
Endpoint detection (EDR)12

Sysmon + Wazuh Endpoint Monitoring Lab

A real Windows 10 endpoint monitored end-to-end: Sysmon telemetry into a Wazuh SIEM, validated with a genuinely causal 6-phase ATT&CK kill chain and 6 custom detection rules, every one verified firing against the live indexer.

SysmonWazuhMITRE ATT&CK
Incident response13

Incident Response Playbooks

Five NIST SP 800-61 / SANS-aligned runbooks — phishing, malware, account compromise, data breach, DDoS — each built as a decision tree, not a checklist, so any analyst on shift handles it the same way.

NIST SP 800-61SANSMarkdownDecision trees
AI / LLM security14

LLM Security Lab

A RAG/agent system built twice — vulnerable and hardened — so every OWASP Top 10 for LLM fix can be demonstrated against the exact same attack, not described in the abstract.

FastAPIPythonRAGPrompt injectionRBAC
Reconstructed incident timeline converging on 17:55:21 UTC
Digital forensics15

Digital Forensics Acquisition Lab

Memory, disk and triage acquired in order of volatility, then a simulated incident reconstructed from Prefetch and the registry — persistence dated to the second by independent sources, with hashes and chain of custody.

FTK ImagerKAPERegistry ExplorerAtomic Red Team
FedSentinel demonstration dashboard overview
AI security research16

FedSentinel

A research prototype of federated learning for intrusion detection, studied as an attack surface: poisoning, free-riding and inference attacks, each paired with its published defence — and a clear account of what it does not prove.

PyTorchFlowerDifferential privacyCKKS

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Happy to walk through the architecture, the failed attempts, or the design decisions.

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