🛡️ WAFinity — Intelligent Web Application Firewall

WAFinity is an Advanced Web Application Firewall (WAF) designed to protect web applications from malicious HTTP traffic.

It combines traditional signature-based detection with machine learning-based anomaly detection to identify both known and previously unseen threats.

🚀 Key Features

• 🚫 Block known web attacks
• 🤖 ML-based anomaly detection
• 🛡️ Real-time HTTP request analysis
• 🔍 Detection of obfuscated and encoded attacks
• 📊 Interactive security insights
• ✨ Modern responsive interface
• ⚡ Fast request processing

🧠 How It Works

WAFinity uses a dual-layer detection approach:

HTTP Request ↓ Signature-Based Detection ↓ Known Attack? ── Yes → BLOCK ↓ No Feature Engineering ↓ ML-Based Anomaly Detection ↓ Malicious? ── Yes → BLOCK ↓ No ALLOW

ML Detection

The machine learning layer analyzes characteristics of incoming HTTP requests to identify anomalous behavior.

Features include:

• Payload entropy
• Parameter length
• Special-character distribution
• Request/payload characteristics

This allows the system to detect obfuscated and previously unseen attack patterns beyond traditional signatures.

🛡️ Threat Detection

Signature-Based Detection

WAFinity detects known attack patterns such as:

• SQL Injection
• Cross-Site Scripting (XSS)
• UNION-based SQL Injection
• JavaScript injection

ML-Based Anomaly Detection

The ML layer analyzes suspicious or obfuscated inputs, including:

• URL-encoded attacks
• Hex-encoded payloads
• Obfuscated JavaScript
• Encoded XSS payloads

📊 Performance

Metric	Result
Threat Detection Precision	95%
Request Response Time	<200 ms
Detection Approach	Hybrid ML + Signature

🖥️ Output Screenshots

📋 Project Management

The development of WAFinity was managed using Jira following an Agile/Scrum workflow.

🏗️ Epic

NeuroShield: Intelligent Web Defense

📌 User Stories

• Develop ML Threat Detection Model — Build an ML model to analyze HTTP requests and detect malicious requests in real time.
• Implement Feature Engineering — Extract relevant features from HTTP requests for effective anomaly detection.
• Implement Signature-Based Detection — Detect and block known web attacks using predefined signatures.
• Integrate ML and Signature Detection — Combine rule-based and ML-based detection into a dual-layered defense system.
• Integrate Detection Engine with Flask — Integrate the detection engine with the Flask application for real-time request analysis.
• Evaluate and Optimize Threat Detection — Evaluate detection accuracy and optimize response time and overall performance.

🔄 Agile Workflow

Epic → User Stories → Subtasks → Sprint → To Do → In Progress → Done

📊 Sprint Metrics

• Sprint: SCRUM Sprint 1
• Total Story Points: 33
• Methodology: Agile/Scrum