Advanced WAF with ML Anomaly Detection · Hacking Project

Detect and block web-layer attacks — including obfuscated and previously unseen payloads — by fusing rule-based inspection with machine-learning anomaly scoring at the reverse pro…

**The problem**
Traditional signature-based Web Application Firewalls (e.g., stock ModSecurity CRS) fail against novel, obfuscated, or zero-day web attacks and generate high false-positive rates that erode analyst trust. Attackers routinely bypass static rulesets with encoding tricks, protocol smuggling, and low-and-slow behavioral attacks that individual requests do not reveal. Enterprises need adaptive detection that learns normal application traffic and flags deviations without manual rule authoring for every threat.

**What you'll build**
Build a reverse-proxy WAF that combines a deterministic rule engine (ModSecurity/Coraza + OWASP CRS) with an ML anomaly-detection layer trained on labeled and unlabeled HTTP traffic features (URI entropy, header ordering, payload token distributions, request timing). Use supervised classifiers for known attack classes and unsupervised models (Isolation Forest, autoencoders) for outlier detection, feeding scored verdicts back into block/allow/challenge decisions with explainability for the SOC.

**Objective** — Detect and block web-layer attacks — including obfuscated and previously unseen payloads — by fusing rule-based inspection with machine-learning anomaly scoring at the reverse proxy.

**Expected outcome** — A deployable containerized WAF proxy with a trained ML model, a tuned OWASP CRS ruleset, a detection dashboard, and a benchmarking report measuring detection rate and false-positive rate against a labeled attack corpus.

**Difficulty** — Advanced — requires simultaneous mastery of HTTP internals, proxy engineering, feature engineering, and supervised/unsupervised ML with adversarial-robustness awareness.

**Primary category** — AppSec; Secondary = AI/ML Security, SOC / Detection Engineering, DevSecOps

**Security domains** — Primary = Blue Team; Secondary = AI Security, DevSecOps, Security Research, AppSec (Purple Team validation)

**Technical focus** — Web Application Firewall, HTTP request parsing, anomaly detection, feature engineering, supervised classification, unsupervised outlier detection, model explainability, reverse-proxy architecture, adversarial ML robustness, detection tuning, log pipeline engineering

**Core technologies** — Languages: Python, Go, Lua; Frameworks/Libraries: scikit-learn, PyTorch/TensorFlow, pandas, FastAPI, OWASP CRS; Security Tools: ModSecurity / Coraza, Nginx/Envoy, OWASP ZAP, sqlmap, ffuf, GoTestWAF; Cloud/Infra: Docker, Kubernetes, Elastic/OpenSearch; AI/Data: Isolation Forest, autoencoders, SHAP for explainability, feature stores; Databases: PostgreSQL, Elasticsearch, Redis

**Where it's valued** — SaaS, e-commerce, fintech, healthcare, managed security service providers (MSSPs), CDN/edge providers, any internet-facing enterprise

**Careers it builds toward** — Detection Engineer, Application Security Engineer, ML Security Engineer, SOC Engineer, Security Researcher, WAF/Edge Security Specialist

**Framework alignment**
• MITRE ATT&CK: Initial Access via Exploit Public-Facing Application (T1190); detection of Command and Scripting Interpreter (T1059) payloads; Application Layer Protocol (T1071) abuse; complements ATLAS for adversarial-ML evasion considerations
• NIST CSF 2.0: Detect (Continuous Monitoring, Adverse Event Analysis), Protect (Platform Security), Respond (Incident Analysis)
• OWASP: Web Top 10 — A03 Injection, A01 Broken Access Control, A10 SSRF; OWASP CRS; API Top 10 API8 Security Misconfiguration
• CIS Controls v8: 13 (Network Monitoring & Defense), 8 (Audit Log Management), 16 (Application Software Security)

**Innovation** — Improved Existing Solution — ML-augmented WAFs exist commercially (Cloudflare, F5, Imperva), but a transparent, explainable, self-hostable hybrid with adversarial-robustness testing is a strong differentiator. Scores: Innovation 7/10, Startup 7/10, Enterprise-Readiness 6/10, Patent = Possible.