Detection of Insider Attacks in Distributed Projected Subgradient Algorithms

TL;DR

This paper proposes AI-based neural network methods, combined with federated learning, to detect and localize malicious agents in distributed optimization algorithms, enhancing robustness and performance.

Contribution

It introduces a neural network approach with federated learning for internal attack detection in distributed algorithms, improving robustness against limited data and mismatched test conditions.

Findings

Neural networks effectively detect malicious agents.

Federated learning enhances model robustness.

AI-based methods outperform score-based detection.

Abstract

The gossip-based distributed algorithms are widely used to solve decentralized optimization problems in various multi-agent applications, while they are generally vulnerable to data injection attacks by internal malicious agents as each agent locally estimates its decent direction without an authorized supervision. In this work, we explore the application of artificial intelligence (AI) technologies to detect internal attacks. We show that a general neural network is particularly suitable for detecting and localizing the malicious agents, as they can effectively explore nonlinear relationship underlying the collected data. Moreover, we propose to adopt one of the state-of-art approaches in federated learning, i.e., a collaborative peer-to-peer machine learning protocol, to facilitate training our neural network models by gossip exchanges. This advanced approach is expected to make our model more robust to challenges with insufficient training data, or mismatched test data. In our simulations, a least-squared problem is considered to verify the feasibility and effectiveness of AI-based methods. Simulation results demonstrate that the proposed AI-based methods are beneficial to improve performance of detecting and localizing malicious agents over score-based methods, and the peer-to-peer neural network model is indeed robust to target issues.