SPECTRA: 5G Intrusion Detection System

Authors

Soumya Sourav Das
Devyanshi Bansal
Sudhanshu Shekhar
Tushar Mathur
Dhruv Dawar

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Overview

SPECTRA is a cross-layer intrusion detection system designed for next-generation 5G networks. Traditional security mechanisms rely heavily on packet-level inspection, which is often insufficient for detecting sophisticated or signal-level attacks.

SPECTRA addresses this limitation by integrating signal processing techniques with network-level analytics to enable early and accurate detection of anomalies in communication systems.

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Key Features

• Cross-layer anomaly detection using both signal-level and network-level features
• Low-latency detection pipeline suitable for real-time systems
• Robust feature extraction using spectral and statistical analysis
• Graph-based modeling for structured anomaly identification
• Scalable architecture adaptable to evolving 5G environments

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System Architecture

The system operates in multiple stages:

1. Signal Acquisition
   Raw communication signals are processed to extract meaningful spectral characteristics.

2. Feature Extraction

   • FFT-based spectral analysis
   • Entropy and statistical feature computation
   • Temporal pattern extraction

3. Graph Modeling
   Network interactions and signal relationships are represented as graphs to capture complex dependencies.

4. Anomaly Detection Engine
   Machine learning models analyze extracted features to identify abnormal patterns and potential threats.

5. Decision Layer
   Combines insights from multiple layers to generate final intrusion alerts with high confidence.

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Methodology

SPECTRA leverages a hybrid approach combining:

• Signal Processing (FFT, spectral density analysis)
• Statistical Analysis (entropy, variance-based metrics)
• Machine Learning for classification and anomaly detection
• Graph-based reasoning for relational insights

This combination enables detection of attacks that are otherwise invisible to conventional packet inspection systems.

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Performance

• Detection Accuracy: 99.1%
• Detection Latency: ~9.6 ms
• Robustness against signal-level and network-level attack vectors

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Applications

• 5G Network Security Monitoring
• Intrusion Detection in Wireless Communication Systems
• Critical Infrastructure Protection
• Telecom Network Analytics

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Tech Stack

• Python
• Signal Processing Libraries (NumPy, SciPy)
• Machine Learning (TensorFlow / PyTorch)
• Data Processing and Analysis Tools

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Repository Structure

.
├── ddos_udp.py            # DDoS-oriented UDP intrusion simulation/detection workflow
├── network_slice.py       # Network slicing scenario implementation
├── net_slice_vul.py       # Network slicing vulnerability scenario script
├── reflected_udp.py       # Reflected UDP attack scenario implementation
├── refudp_v2.py           # Reflected UDP scenario variant
└── README.md

Research Limitation Note

Detailed mitigation plans are intentionally not included in this repository due to ongoing research and evaluation constraints.

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Future Work

• Integration with real-time telecom infrastructure
• Hardware acceleration for ultra-low latency detection
• Expansion to 6G communication paradigms
• Adaptive learning for evolving threat landscapes

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License

This project is intended for research and academic purposes. Licensing terms may be updated based on future development and potential intellectual property considerations.

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Acknowledgements

We acknowledge the support of the 5G Hackathon platform and contributors who provided insights into modern communication system challenges.