Privacy-Aware Cyberterrorism Network Analysis using Graph Neural Networks and Federated Learning

TL;DR

This paper introduces PA-FGNN, a privacy-preserving federated graph neural network framework that effectively analyzes cyberterrorism networks while safeguarding sensitive data through encryption and differential privacy.

Contribution

It presents a novel integration of graph attention networks, differential privacy, and homomorphic encryption within a federated learning setup for cyber threat analysis.

Findings

Achieves over 91% classification accuracy on simulated cyber graphs.

Maintains robustness with 20% adversarial client participation.

Uses less than 18% communication overhead.

Abstract

Cyberterrorism poses a formidable threat to digital infrastructures, with increasing reliance on encrypted, decentralized platforms that obscure threat actor activity. To address the challenge of analyzing such adversarial networks while preserving the privacy of distributed intelligence data, we propose a Privacy-Aware Federated Graph Neural Network (PA-FGNN) framework. PA-FGNN integrates graph attention networks, differential privacy, and homomorphic encryption into a robust federated learning pipeline tailored for cyberterrorism network analysis. Each client trains locally on sensitive graph data and exchanges encrypted, noise-perturbed model updates with a central aggregator, which performs secure aggregation and broadcasts global updates. We implement anomaly detection for flagging high-risk nodes and incorporate defenses against gradient poisoning. Experimental evaluations on simulated dark web and cyber-intelligence graphs demonstrate that PA-FGNN achieves over 91\% classification accuracy, maintains resilience under 20\% adversarial client behavior, and incurs less than 18\% communication overhead. Our results highlight that privacy-preserving GNNs can support large-scale cyber threat detection without compromising on utility, privacy, or robustness.