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

TL;DR

This paper introduces a game-theoretic framework for enhancing the security of distributed sensor networks against adversarial attacks, focusing on decision fusion, Byzantine defense, and consensus protection.

Contribution

It presents novel game-theoretic strategies and algorithms for adversarial information fusion, including defense schemes and message passing methods, in distributed sensor networks.

Findings

Developed a soft isolation defense scheme against Byzantines.

Proposed an optimal decision fusion strategy considering adversaries.

Introduced a low-complexity message passing algorithm for near-optimal fusion.

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.