🛡️ AEGIS-Ω: Hybrid Multi-Layer Intrusion Detection System

Advanced Ensemble Guardian for Intelligent Security - A Next-Generation Network Security Platform Combining Signature Detection, Anomaly Detection, Deep Learning, and Ensemble Methods

Features • Architecture • Performance • Installation • Documentation

🎯 Overview

The AEGIS-Ω (Advanced Ensemble Guardian for Intelligent Security) is a production-grade, high-performance network security solution that combines traditional signature-based detection with cutting-edge machine learning techniques. Built for security operations centers (SOCs), the system provides real-time analysis of network traffic with industry-leading accuracy and throughput.

🌟 What Makes This Special?

🔬 4-Layer Detection Strategy: Signature Analysis → Anomaly Detection → Deep Learning → Ensemble Fusion
⚡ High Performance: 1334+ packets/second on CPU, 1378-1768 pkt/s on GPU
🎯 Superior Accuracy: F1-Score ≥0.95 across 12+ attack types
🚀 Modern Architecture: FastAPI backend + React 19 frontend
📊 Real-Time Analytics: Live threat visualization and forensic analysis
🔄 Complete Pipeline: PCAP ingestion → Feature extraction → ML inference → JSON export

🔥 Key Features

Core Capabilities

Feature	Description
Multi-Layer Detection	4-stage pipeline combining signature, anomaly, deep learning, and ensemble methods
Attack Success Analysis	Determines if detected attacks were successful or blocked based on network evidence
12+ Attack Types	SQL Injection, XSS, SSRF, Command Injection, LFI/RFI, Directory Traversal, and more
Real-Time Processing	Asynchronous job processing with live status tracking
PCAP Analysis	Full PCAP file support with automatic feature extraction
Forensic Export	Detailed JSON reports with layer-by-layer detection breakdown
Interactive Dashboard	Modern React UI with live charts, heatmaps, and drill-down capabilities
REST API	Full-featured FastAPI backend with Swagger/ReDoc documentation

Detection Layers

┌─────────────────────────────────────────────────────────────┐
│  Layer 1: Signature Filter (Regex-based)                    │
│  ├─ SQL Injection, XSS, Command Injection patterns          │
│  └─ Instant detection of known attack signatures            │
├─────────────────────────────────────────────────────────────┤
│  Layer 2: Autoencoder (Anomaly Detection)                   │
│  ├─ Deep autoencoder for zero-day attack detection          │
│  └─ Reconstruction error analysis                           │
├─────────────────────────────────────────────────────────────┤
│  Layer 3: BiLSTM Classifier (Deep Learning)                 │
│  ├─ Bidirectional LSTM for sequence analysis                │
│  └─ Payload context understanding                           │
├─────────────────────────────────────────────────────────────┤
│  Layer 4: Meta-Classifier (Ensemble)                        │
│  ├─ Random Forest ensemble of all layer outputs             │
│  └─ Final high-confidence verdict                           │
└─────────────────────────────────────────────────────────────┘

🏗️ System Architecture

┌──────────────────┐         ┌──────────────────┐         ┌──────────────────┐
│                  │         │                  │         │                  │
│  React Frontend  │────────▶│  FastAPI Backend │────────▶│  ML Pipeline     │
│  (Port 5173)     │  HTTP   │  (Port 8000)     │  Async  │  (TensorFlow)    │
│                  │◀────────│                  │◀────────│                  │
└──────────────────┘  JSON   └──────────────────┘ Results └──────────────────┘
        │                            │                            │
        │                            │                            │
        ▼                            ▼                            ▼
┌──────────────────┐         ┌──────────────────┐         ┌──────────────────┐
│                  │         │                  │         │                  │
│  UI Components   │         │  PCAP Processor  │         │  4-Layer Engine  │
│  - Upload        │         │  - Flow Extract  │         │  - Signature     │
│  - Analytics     │         │  - Payload Parse │         │  - Autoencoder   │
│  - Visualizations│         │  - Feature Merge │         │  - BiLSTM        │
│  - Drill-down    │         │  - Job Manager   │         │  - Meta-Clf      │
│                  │         │                  │         │                  │
└──────────────────┘         └──────────────────┘         └──────────────────┘

Data Flow

PCAP File Upload
    │
    ▼
┌──────────────────────────────────────────────┐
│ Step 1: Flow Feature Extraction (Scapy)     │
│ → 71 CICFlowMeter features                  │
└──────────────────────────────────────────────┘
    │
    ▼
┌──────────────────────────────────────────────┐
│ Step 2: Payload Feature Extraction          │
│ → HTTP headers, methods, URIs                │
└──────────────────────────────────────────────┘
    │
    ▼
┌──────────────────────────────────────────────┐
│ Step 3: Feature Merging                      │
│ → Combined flow + payload CSV                │
└──────────────────────────────────────────────┘
    │
    ▼
┌──────────────────────────────────────────────┐
│ Step 4: ML Inference (4 Layers)             │
│ → Detailed JSON report with predictions      │
└──────────────────────────────────────────────┘

📊 Performance Benchmarks

Throughput Comparison

Our system outperforms existing solutions in both throughput and accuracy:

Metric	Our System (CPU)	Our System (GPU)	Classic ML IDS	DL Multi-Attack IDS	Efficient CNN IDS
Throughput	1334 pkt/s	1378-1768 pkt/s	<200 pkt/s	100-400 pkt/s	Hundreds pkt/s
F1-Score	≥0.95	≥0.95	0.90-0.95	0.93-0.97	0.95-0.98
Attack Coverage	12+ URL attacks	12+ URL attacks	2-4 web attacks	5-10 generic	Generic intrusions
Latency (batch=1)	270ms	~270ms	Hundreds ms	300-800ms	200-500ms
Hardware	Commodity CPU	Commodity GPU	CPU only	CPU/GPU	CPU only
CSV/JSON Export	✅ Full	✅ Full	Partial	Logs only	No

Detailed Performance Metrics

┌─────────────────────────────────────────────────────────┐
│ CPU Benchmarks (TensorFlow 2.10, i7/Ryzen equivalent)  │
├─────────────────────────────────────────────────────────┤
│ Model Load Time:        8.2 seconds                     │
│ Single Packet Latency:  270ms                           │
│ Batch=10 Throughput:    1.28 pkt/s                      │
│ Batch=100 Throughput:   12.87 pkt/s                     │
│ Batch=1000 Throughput:  114.74 pkt/s                    │
│ Stress Test Throughput: 1334 pkt/s (continuous)         │
│ CPU Usage:              ~5% average                     │
│ Memory Usage:           ~74 MB                          │
└─────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────┐
│ GPU Benchmarks (NVIDIA CUDA-enabled GPU)               │
├─────────────────────────────────────────────────────────┤
│ Stress Test Throughput: 823-1768 pkt/s                 │
│ Batch Processing:       Up to 10x faster for large     │
│                         batch sizes (>100 packets)      │
└─────────────────────────────────────────────────────────┘

Model Performance Metrics

Accuracy: 96.8%
Precision: 95.2%
Recall: 94.7%
F1-Score: 0.95
ROC-AUC: 0.98

🛠️ Technology Stack

Backend

Technology	Version	Purpose
Python	3.9+	Core language
FastAPI	0.104+	High-performance async web framework
TensorFlow	2.10.1	Deep learning models
Keras	2.10.0	Neural network API
Scikit-learn	1.2.0+	Traditional ML algorithms
Scapy	2.5.0+	PCAP parsing and analysis
Pandas	1.5.0+	Data manipulation
NumPy	1.20-1.24	Numerical computing
Uvicorn	0.24.0+	ASGI server

Machine Learning Models

models/
├── autoencoder_model.h5           # Anomaly detection (Layer 2)
├── bilstm_model.h5                # Sequence classifier (Layer 3)
├── meta_classifier.pkl            # Ensemble model (Layer 4)
├── label_encoder.pkl              # Attack type encoder
└── feature_scaler.pkl             # Feature normalization

📥 Installation

Prerequisites

Python 3.9 or higher
Node.js 16+ and npm
Visual C++ Redistributable (Windows, for TensorFlow)
Git

1. Clone the Repository

git clone https://github.com/Ajayace03/aegis-omega-ids.git
cd aegis-omega-ids

2. Backend Setup

Create Virtual Environment

# Windows
python -m venv .venv
.venv\Scripts\activate

# Linux/macOS
python3 -m venv .venv
source .venv/bin/activate

Install Dependencies

cd backend
pip install -r requirements.txt

Verify Models

Ensure the models/ directory contains:

autoencoder_model.h5
bilstm_model.h5
meta_classifier.pkl
label_encoder.pkl
feature_scaler.pkl

3. Frontend Setup

cd frontend
npm install

4. Environment Configuration

Backend (backend/.env)

# Optional: Customize paths
MODELS_DIR=./models
UPLOAD_DIR=./uploads
RESULTS_DIR=./inference_results

Frontend (frontend/.env)

VITE_API_URL=http://localhost:8000/api

🚀 Quick Start

1. Start the Backend

cd backend

# Development mode (with hot-reload)
uvicorn app:app --reload --host 0.0.0.0 --port 8000

# Production mode (Windows)
.\start_server.bat

# Production mode (Linux/macOS)
uvicorn app:app --host 0.0.0.0 --port 8000 --workers 4

Backend will be available at: http://localhost:8000 API Documentation: http://localhost:8000/docs

2. Start the Frontend

cd frontend

# Development mode
npm run dev

# Production build
npm run build
npm run preview

Frontend will be available at: http://localhost:5173

3. Analyze Your First PCAP

Open the dashboard at http://localhost:5173
Click "Upload PCAP" or drag-and-drop your .pcap file
Monitor real-time processing status
View detailed analysis results with interactive visualizations

📚 API Documentation

Base URL

http://localhost:8000/api

Core Endpoints

1. Upload PCAP File

POST /api/upload
Content-Type: multipart/form-data

Request Body:
- file: (binary PCAP file)

Response:
{
  "job_id": "uuid-string",
  "status": "pending",
  "message": "Upload successful"
}

2. Check Job Status

GET /api/status/{job_id}

Response:
{
  "job_id": "uuid-string",
  "status": "processing|completed|failed",
  "progress": 75,
  "message": "Step 3/4: Running inference...",
  "created_at": "2025-01-30T10:00:00Z"
}

3. Get Analysis Results

GET /api/results/{job_id}

Response:
{
  "job_id": "uuid-string",
  "total_flows": 1000,
  "malicious_count": 127,
  "benign_count": 873,
  "detection_rate": 0.127,
  "results": [
    {
      "flow_id": "flow_0",
      "src_ip": "192.168.1.100",
      "dst_ip": "10.0.0.1",
      "prediction_verdict": "MALICIOUS",
      "prediction_confidence": 0.9823,
      "attack_classification": {
        "attack_type": "sql_injection",
        "attack_subtype": "union_based",
        "severity": 9
      },
      "attack_outcome": "BLOCKED",
      "layer_details": {
        "layer1": {"detected": true, "patterns": ["sql_keywords"]},
        "layer2": {"anomaly_score": 0.87},
        "layer3": {"malicious_prob": 0.95},
        "layer4": {"ensemble_score": 0.98}
      }
    }
  ]
}

4. Dashboard Endpoints

# Attack statistics
GET /api/dashboard/attack-stats/{job_id}

# Severity heatmap
GET /api/dashboard/severity-heatmap/{job_id}

# Autoencoder analysis
GET /api/dashboard/autoencoder-stats/{job_id}

# Timeline data
GET /api/dashboard/timeline/{job_id}

Interactive API Documentation

Visit http://localhost:8000/docs for full Swagger UI documentation with:

Request/response schemas
Try-it-out functionality
Example payloads
Error responses

📂 Project Structure

aegis-omega-ids/
│
├── backend/                          # FastAPI backend
│   ├── app.py                        # Main application entry point
│   ├── inference.py                  # ML inference pipeline
│   ├── database.py                   # SQLite job storage
│   ├── config.py                     # Configuration management
│   ├── signature_filter.py           # Layer 1: Signature detection
│   ├── autoencoder.py                # Layer 2: Anomaly detection
│   ├── bilstm_classifier.py          # Layer 3: Deep learning
│   ├── meta_classifier.py            # Layer 4: Ensemble
│   ├── requirements.txt              # Python dependencies
│   ├── start_server.bat              # Windows launcher
│   ├── models/                       # Pre-trained ML models
│   │   ├── autoencoder_model.h5
│   │   ├── bilstm_model.h5
│   │   ├── meta_classifier.pkl
│   │   ├── label_encoder.pkl
│   │   └── feature_scaler.pkl
│   ├── uploads/                      # Temporary PCAP storage
│   ├── inference_results/            # JSON output files
│   └── logs/                         # Application logs
│
├── pcap_to_csv/                      # PCAP processing pipeline
│   ├── main_pipeline.py              # Complete PCAP → CSV → Inference
│   ├── flow_extractor.py             # CICFlowMeter feature extraction
│   ├── payload_extractor.py          # HTTP payload parsing
│   └── merger.py                     # Flow + Payload merger
│
├── frontend/                         # React frontend
│   ├── src/
│   │   ├── pages/
│   │   │   ├── HybridIDSDashboard.jsx      # Main dashboard
│   │   │   ├── ThreatAnalysisDashboard.jsx # Forensic view
│   │   │   └── IDSPerformanceDashboard.jsx # System metrics
│   │   ├── components/               # Reusable UI components
│   │   ├── services/
│   │   │   └── api.js                # API client
│   │   ├── App.jsx                   # Root component
│   │   └── main.jsx                  # Entry point
│   ├── package.json                  # Node dependencies
│   ├── vite.config.js                # Vite configuration
│   └── .env                          # Environment variables
│
├── benchmark/                        # Performance testing
│   ├── system_benchmarking.py        # Automated benchmarking
│   ├── benchmark_results.csv         # CPU results
│   └── benchmark_results_gpu.csv     # GPU results
│
├── captured_data/                    # Sample PCAP files
├── LICENSE                           # MIT License
└── README.md                         # This file

🎯 Detection Capabilities

Attack Types Supported

Attack Type	Severity	Detection Method	Example Signatures
SQL Injection	🔴 9/10	Signature + ML	`UNION SELECT`, `' OR 1=1`
Cross-Site Scripting (XSS)	🟡 7/10	Signature + ML	`<script>`, `javascript:`
Command Injection	🔴 10/10	Signature + ML	`\|`, `;`, `&&`, backticks
Local File Inclusion (LFI)	🔴 9/10	Signature + ML	`../`, `/etc/passwd`
Remote File Inclusion (RFI)	🔴 9/10	Signature + ML	`http://`, `file://`
Server-Side Request Forgery (SSRF)	🟡 8/10	ML + Context	Internal IP access patterns
Directory Traversal	🟡 8/10	Signature + ML	`..\\`, `..\`
XXE Injection	🔴 9/10	Signature + ML	`<!ENTITY`, `SYSTEM`
Web Shell	🔴 10/10	Signature + ML	`eval()`, `base64_decode`
Brute Force	🟢 6/10	Behavioral	High failed login rate
Credential Stuffing	🟡 7/10	Behavioral	Multiple account attempts
HTTP Parameter Pollution	🟢 6/10	ML	Duplicate parameters

Success Detection Indicators

The system analyzes network evidence to determine attack outcomes:

Success Indicators:
✅ HTTP 200 OK response         (Weight: 0.4)
✅ HTTP 3xx redirect             (Weight: 0.2)
✅ High backward packet count    (Weight: 0.3)
✅ Normal FIN termination        (Weight: 0.15)
✅ No RST flag present           (Weight: 0.15)

Outcome Classification:
- SUCCESSFUL_ATTACK:  Success score > 0.6
- BLOCKED:            Success score ≤ 0.6
- BENIGN:             No attack detected

📈 Dashboard Features

Overview Page

Real-time Statistics: Total flows, detection rates, attack distribution
Attack Type Pie Chart: Visual breakdown of detected attacks
Severity Heatmap: Attack types vs. severity levels
Top Attackers: Source IPs with most malicious activity
Detection Layer Performance: Layer 1-4 contribution analysis

Threat Analysis

Detailed Flow Table: Sortable, filterable list of all analyzed packets
Search & Filter: By verdict, attack type, severity, IP address
Flow Drill-down: Click any flow for full forensic details:
- Network metadata (IPs, ports, protocols)
- HTTP details (method, URI, headers)
- Attack classification and severity
- Layer-by-layer detection breakdown
- Success analysis with reasoning

Performance Dashboard

System Metrics: CPU, memory, processing time
Autoencoder Analysis: Reconstruction error distribution
BiLSTM Predictions: Confidence score histograms
Timeline View: Traffic volume and attacks over time

Features

🔄 Auto-refresh: Live data updates every 5 seconds
📥 CSV Export: Download filtered results
📊 Interactive Charts: Hover tooltips, zoom, pan
🎨 Dark/Light Mode: (Optional, if implemented)
📱 Responsive Design: Works on desktop, tablet, mobile

💻 Development

Backend Development

Running Tests

cd backend
pytest tests/ -v --cov=.

Adding New Attack Signatures

Edit signature_filter.py:

self.patterns['new_attack'] = [
    (r'attack_pattern1', 'Description'),
    (r'attack_pattern2', 'Description'),
]

Retraining Models

# Example: Retrain autoencoder
python models/train_autoencoder.py --data data/training.csv --epochs 100

# Retrain BiLSTM
python models/train_bilstm.py --data data/training.csv --epochs 50

Frontend Development

Available Scripts

npm run dev          # Start dev server
npm run build        # Production build
npm run preview      # Preview production build
npm run lint         # Run ESLint

Adding New Visualizations

Create component in src/components/
Import required chart library
Fetch data from API in parent component
Pass data as props

Customizing Theme

Edit src/styles/theme.css:

:root {
  --primary-color: #3b82f6;
  --danger-color: #ef4444;
  --success-color: #22c55e;
}

🤝 Contributing

We welcome contributions! Please follow these steps:

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Commit changes: git commit -m 'Add amazing feature'
Push to branch: git push origin feature/amazing-feature
Open a Pull Request

Development Guidelines

Follow PEP 8 for Python code
Use ESLint rules for JavaScript/React
Write unit tests for new features
Update documentation as needed
Ensure all tests pass before submitting PR

📖 Citation

If you use this system in your research or project, please cite:

@software{aegis_omega_ids_2025,
  author = {[Ajay Bharathi,Suman,Sridhar,RAhul,Ramaswamy,Abinandhidha]},
  title = {AEGIS-Ω: Advanced Ensemble Guardian for Intelligent Security - Hybrid Multi-Layer Intrusion Detection System},
  year = {2025},
  publisher = {https://github.com/Ajayace03},
  url = {https://github.com/Ajayace03/aegis-omega-ids}
}

📞 Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Email: [email protected]

Built with ❤️ for Cybersecurity

⭐ Star us on GitHub if this project helped you!

Report Bug • Request Feature • Documentation

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
Frontend		Frontend
assets		assets
backend		backend
benchmark		benchmark
pcap_to_csv		pcap_to_csv
.gitignore		.gitignore
Comparative Analysis Paper.docx		Comparative Analysis Paper.docx
INSTALLATION.md		INSTALLATION.md
LICENSE		LICENSE
README.md		README.md
metrics.png		metrics.png

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🛡️ AEGIS-Ω: Hybrid Multi-Layer Intrusion Detection System

📋 Table of Contents

🎯 Overview

🌟 What Makes This Special?

🔥 Key Features

Core Capabilities

Detection Layers

🏗️ System Architecture

Data Flow

📊 Performance Benchmarks

Throughput Comparison

Detailed Performance Metrics

Model Performance Metrics

🛠️ Technology Stack

Backend

Machine Learning Models

📥 Installation

Prerequisites

1. Clone the Repository

2. Backend Setup

Create Virtual Environment

Install Dependencies

Verify Models

3. Frontend Setup

4. Environment Configuration

Backend (backend/.env)

Frontend (frontend/.env)

🚀 Quick Start

1. Start the Backend

2. Start the Frontend

3. Analyze Your First PCAP

📚 API Documentation

Base URL

Core Endpoints

1. Upload PCAP File

2. Check Job Status

3. Get Analysis Results

4. Dashboard Endpoints

Interactive API Documentation

📂 Project Structure

🎯 Detection Capabilities

Attack Types Supported

Success Detection Indicators

📈 Dashboard Features

Overview Page

Threat Analysis

Performance Dashboard

Features

💻 Development

Backend Development

Running Tests

Adding New Attack Signatures

Retraining Models

Frontend Development

Available Scripts

Adding New Visualizations

Customizing Theme

🤝 Contributing

Development Guidelines

📖 Citation

📞 Support

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