Deep Learning for Resilient Adversarial Decision Fusion in Byzantine Networks

TL;DR

This paper presents a deep learning framework for resilient decision fusion in adversarial sensor networks, capable of handling diverse attack scenarios without scenario-specific tuning, outperforming traditional methods in accuracy and efficiency.

Contribution

The paper introduces a deep neural network approach that generalizes across multiple Byzantine attack scenarios, eliminating the need for explicit parameter tuning and scenario-specific assumptions.

Findings

Achieves higher accuracy than traditional methods in simulations

Demonstrates robustness against various Byzantine attack strategies

Ensures scalability and real-time computational efficiency

Abstract

This paper introduces a deep learning-based framework for resilient decision fusion in adversarial multi-sensor networks, providing a unified mathematical setup that encompasses diverse scenarios, including varying Byzantine node proportions, synchronized and unsynchronized attacks, unbalanced priors, adaptive strategies, and Markovian states. Unlike traditional methods, which depend on explicit parameter tuning and are limited by scenario-specific assumptions, the proposed approach employs a deep neural network trained on a globally constructed dataset to generalize across all cases without requiring adaptation. Extensive simulations validate the method's robustness, achieving superior accuracy, minimal error probability, and scalability compared to state-of-the-art techniques, while ensuring computational efficiency for real-time applications. This unified framework demonstrates the potential of deep learning to revolutionize decision fusion by addressing the challenges posed by Byzantine nodes in dynamic adversarial environments.