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Detection-struts-cve-2017-5638-detector — Real-time anomaly detection system for Apache Struts CVE-2017-5638 exploit using streaming analytics, 3-gram byte analysis, and Count-Min Sketch. Detects RCE attacks without signatures, with <5ms latency and <0.1% false positives. | Kitploit
Tools/GitHubGitHub/joidiego/detection-struts-cve-2017-5638-detector
Vulnerability AnalysisExploitationWeb SecurityMachine LearningIntrusion DetectionAnomaly Detection
GitHubjoidiego/detection-struts-cve-2017-5638-detector

Detection-struts-cve-2017-5638-detector

Real-time anomaly detection system for Apache Struts CVE-2017-5638 exploit using streaming analytics, 3-gram byte analysis, and Count-Min Sketch. Detects RCE attacks without signatures, with <5ms latency and <0.1% false positives.

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51 year agoNot yet reviewed

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🚨 Real-Time Anomaly Detection for Apache Struts CVE-2017-5638

Zero-Day Exploit Detection Based on Streaming Analytics & Probabilistic Data Structures

Dashboard Screenshot Dashboard Screenshot

📌 Project Description

This project is a real-time anomaly detection system designed to detect RCE (Remote Code Execution) exploits on Apache Struts through the CVE-2017-5638 vulnerability, without relying on known signatures or patterns.

CVE-2017-5638 is a critical vulnerability used in the 2017 Equifax attack, which led to the personal data breach of 147 million people. This attack succeeded due to patch management failure and lack of early detection.

This system addresses those weaknesses by:

  • ✅ Behavior-based detection (not signature-based)
  • ✅ Byte-level 3-gram analysis of the entire HTTP request
  • ✅ Count-Min Sketch for efficient frequency tracking
  • ✅ KL Divergence to measure deviation from normal patterns
  • ✅ Adaptive threshold (mean + 3σ) for real-time detection
  • ✅ Visual dashboard with Streamlit

Goal: detect zero-day exploits before the system is compromised, even if the payload has never been seen before.


🎯 Main Objectives

  • Detect Struts exploits in real-time with latency ≤ 5ms
  • Memory ≤ 1 GB even under throughput ≥ 50,000 requests/second
  • False positive rate < 0.1% on normal data
  • Detection rate ≥ 95% on synthetic exploit payloads
  • System deployable and monitorable in real-time

Steps to Run

  • Run the log simulator (50k req/sec, 60 seconds) python scripts/simulate_logs.py
  • Run the detection system python src/main.py
  • Open the real-time dashboard in a new terminal streamlit run scripts/dashboard.py
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