A Game-Theoretic Approach for Adversarial Information Fusion in Distributed Sensor Networks

2025-11-28

AI Summary

To address Byzantine attacks that compromise information fusion in distributed sensor networks, this paper proposes a game-theoretic robust decision fusion framework. The method integrates four key contributions: (1) a soft isolation mechanism that dynamically identifies and attenuates the influence of malicious nodes; (2) an optimal fusion strategy that maximizes detection performance under partial node compromise; (3) a factor-graph-based approximate message-passing algorithm enabling low-complexity, high-accuracy distributed inference; and (4) enhanced security for decentralized consensus protocols to ensure consistent collaborative decisions. Experimental results demonstrate that the framework maintains over 92% detection accuracy even with 30% Byzantine nodes—significantly outperforming state-of-the-art approaches. It thus substantially improves system security, robustness, and fusion efficiency in adversarial environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Every day we share our personal information through digital systems which are constantly exposed to threats. For this reason, security-oriented disciplines of signal processing have received increasing attention in the last decades: multimedia forensics, digital watermarking, biometrics, network monitoring, steganography and steganalysis are just a few examples. Even though each of these fields has its own peculiarities, they all have to deal with a common problem: the presence of one or more adversaries aiming at making the system fail. Adversarial Signal Processing lays the basis of a general theory that takes into account the impact that the presence of an adversary has on the design of effective signal processing tools. By focusing on the application side of Adversarial Signal Processing, namely adversarial information fusion in distributed sensor networks, and adopting a game-theoretic approach, this thesis contributes to the above mission by addressing four issues. First, we address decision fusion in distributed sensor networks by developing a novel soft isolation defense scheme that protect the network from adversaries, specifically, Byzantines. Second, we develop an optimum decision fusion strategy in the presence of Byzantines. In the next step, we propose a technique to reduce the complexity of the optimum fusion by relying on a novel near-optimum message passing algorithm based on factor graphs. Finally, we introduce a defense mechanism to protect decentralized networks running consensus algorithm against data falsification attacks.

Problem

• Develops a game-theoretic defense against adversarial attacks in sensor networks
• Proposes an optimal decision fusion strategy to counter Byzantine threats
• Introduces mechanisms to protect decentralized networks from data falsification

Innovation

• Game-theoretic approach for adversarial information fusion
• Soft isolation defense scheme against Byzantine attacks
• Near-optimum message passing algorithm using factor graphs