面向信息物理系统高效可持续异常检测的混合时序差分一致性自编码器
Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems
AI总结：
本文提出混合时序差分一致性自编码器 hybrid TDC-AE，通过结合确定性节点与统计节点捕捉系统动态，在无需领域知识的情况下将异常检测时间缩短3%，并超越BATADAL挑战赛领先者，实现高效可持续的CPS异常检测。
摘要：
由于快速数字化以及物联网设备与工业控制系统（ICS）的集成，针对关键基础设施（尤其是配水系统）的网络攻击日益增多。这些信息物理系统（CPS）引入了新的脆弱性，因此需要鲁棒且自动化的入侵检测系统（IDS）来缓解潜在威胁。本研究通过利用传感器数据中的时间相关性、将物理原理融入机器学习模型以及优化面向边缘应用的计算效率，来解决异常检测中的关键挑战。我们在时序差分一致性（TDC）损失概念的基础上捕捉系统动态，确保动态状态之间存在有意义的关系。在此基础之上，我们提出了一种基于混合自编码器的方法，称为 hybrid TDC-AE，该方法通过同时引入确定性节点和传统统计节点来扩展 TDC。这种混合结构使模型能够考虑非确定性过程。我们的方法取得了最先进的分类性能，同时将异常检测时间缩短了 3%，在不依赖领域特定知识的情况下超越了 BATADAL 挑战赛领先者，因而具有广泛的适用性。此外，该方法保持了传统自编码器的计算效率，同时减少了全连接层的数量，从而形成了一种更可持续、更高效的解决方案。该方法展示了利用受物理启发的致性原则如何增强异常检测并提升信息物理系统的韧性。
英文摘要：
Cyberattacks on critical infrastructure, particularly water distribution systems, have increased due to rapid digitalization and the integration of IoT devices and industrial control systems (ICS). These cyber-physical systems (CPS) introduce new vulnerabilities, requiring robust and automated intrusion detection systems (IDS) to mitigate potential threats. This study addresses key challenges in anomaly detection by leveraging time correlations in sensor data, integrating physical principles into machine learning models, and optimizing computational efficiency for edge applications. We build upon the concept of temporal differential consistency (TDC) loss to capture the dynamics of the system, ensuring meaningful relationships between dynamic states. Expanding on this foundation, we propose a hybrid autoencoder-based approach, referred to as hybrid TDC-AE, which extends TDC by incorporating both deterministic nodes and conventional statistical nodes. This hybrid structure enables the model to account for non-deterministic processes. Our approach achieves state-of-the-art classification performance while improving time to detect anomalies by 3%, outperforming the BATADAL challenge leader without requiring domain-specific knowledge, making it broadly applicable. Additionally, it maintains the computational efficiency of conventional autoencoders while reducing the number of fully connected layers, resulting in a more sustainable and efficient solution. The method demonstrates how leveraging physics-inspired consistency principles enhances anomaly detection and strengthens the resilience of cyber-physical systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
