arXiv 2605.158742026-05-15cs.LG

Ti-iLSTM：一种用于工业水处理系统逻辑层异常检测的TinyDL方法

Ti-iLSTM: A TinyDL Approach for Logic-Level Anomaly Detection in Industrial Water Treatment Systems

University of Waikato（怀卡托大学）；Auckland University of Technology（奥克兰理工大学）；Aalto University（阿尔托大学）；Luleå University of Technology（卢勒奥理工大学）

Mandar Joshi, Farzana Zahid, Judy Bowen, Matthew M. Y. Kuo, Valeriy Vyatkin, Emil Karlsson

备注：: NA

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AI总结：

本文提出Ti-iLSTM框架，利用TinyDL实现轻量级设备端逻辑层异常检测，通过优化LSTM内存和空间占用，在SWaT和WADI数据集上均表现出高检测性能。

摘要：

工业水处理系统（IWTS）是安全关键的网络物理基础设施，由于连接性增加，这些系统面临能够操纵过程行为而不产生明显设备异常的网络威胁。特别是逻辑层欺骗性攻击可以保持数值合理的测量值，同时破坏预期的因果关系。这些攻击难以通过基于阈值的监控检测，或需要重型服务器导向的异常检测模型。本文探讨了Tiny Deep Learning（TinyDL）在为资源受限的可编程逻辑控制器（PLCs）提供轻量级设备端逻辑层异常检测方面的潜力。我们提出了一种新的框架，基于TinyDL的增量LSTM（Ti-iLSTM），优化了长短期记忆（LSTM）的内存和空间占用，以检测基于可编程逻辑控制器（PLC）的工业水处理系统（IWTS）中的逻辑层不一致。在公开可用的SWaT数据集上的实验表明，优化后的模型实现了高检测性能（F1-score=0.983和ROC-AUC=0.998）。在WADI数据集上的部署式验证证实，所提出的轻量级框架在单个数据集之外仍然适用。研究证明，结合逻辑意识监督与Tiny Deep Learning（TinyDL）序列学习，可以创建一种适用于工业环境资源受限可编程逻辑控制器（PLCs）的高效且准确的异常检测方法。

英文摘要：

Industrial Water Treatment Systems (IWTS) are safety critical cyber-physical infrastructures and due to increased connectivity, these systems are exposed to cyber threats that can manipulate process behaviour without creating obvious devices outliers. In particular, logic-layer deception anomalies can preserve numerically plausible measurements while breaking expected cause-and-effect relationships in the control process. These attacks are difficult to detect using threshold-based monitoring or require heavy server-oriented anomaly detection models. This paper explores the potential of Tiny Deep Learning (TinyDL) to provide lightweight on-device logic-level anomaly detection for resource constrained Programmable Logic Controllers (PLCs). We propose a novel framework, TinyDL-based incremental LSTM (Ti-iLSTM) which optimises the memory and space foot print of Long Short-Term Memory (LSTM), to detect logic-layer inconsistencies in Programmable Logic Controller (PLC) based Industrial Water Treatment Systems (IWTS). Experiments on the publicly available SWaT dataset show that the optimised model achieves high detection performance (F1-score=0.983 and ROC-AUC=0.998). A deployment-style validation on the WADI dataset confirms that the proposed light-weight framework remains applicable beyond a single dataset. The research demonstrates that combining logic-aware supervision with Tiny Deep Learning (TinyDL) sequence learning creates an efficient and accurate anomaly detection suitable for resource constrained Programmable Logic Controllers (PLCs) in industrial environments.

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