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.

翻译：本文提出了一种基于深度学习的弹性决策融合框架，用于对抗性多传感器网络，该框架提供了一个统一的数学设置，涵盖了多种场景，包括变化的拜占庭节点比例、同步与非同步攻击、先验不平衡、自适应策略以及马尔可夫状态。与传统方法依赖于显式参数调优且受限于特定场景假设不同，所提出的方法采用在全局构建的数据集上训练的深度神经网络，无需调整即可泛化至所有情况。大量仿真验证了该方法的鲁棒性，与现有先进技术相比，实现了更高的准确性、更低的错误概率和更好的可扩展性，同时确保了实时应用的计算效率。这一统一框架展示了深度学习通过应对动态对抗环境中拜占庭节点带来的挑战，革新决策融合的潜力。