用深度学习统一解决传感器网络中的拜占庭攻击问题
Deep Learning for Resilient Adversarial Decision Fusion in Byzantine Networks
- 构建全局数据集训练神经网络，无需针对不同场景调参
- 仿真显示误差率极低，性能优于现有方法且可实时运行
- 适合动态对抗环境下的高可靠性决策融合系统
本文提出一种基于深度学习的鲁棒决策融合框架，用于对抗性多传感器网络。该框架提供统一的数学建模，涵盖不同拜占庭节点比例、同步/异步攻击、非平衡先验、自适应策略及马尔可夫状态等多种场景。与依赖显式参数调优的传统方法不同，该方法通过全局构建数据集训练深度神经网络，实现跨所有场景的泛化能力，无需额外调整。大量仿真验证了其鲁棒性：在准确率、最小错误概率和可扩展性方面均优于现有技术，同时保证实时应用所需的计算效率。该统一框架展示了深度学习在应对动态对抗环境中拜占庭节点挑战方面的潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。