一句话：提出KDDT，结合语言模型与LSTM提取上下文及时序特征，并用知识蒸馏扩充数据，实现TCMS异常检测。
网络物理系统（CPS）如列车控制与管理系统（TCMS）是安全关键基础设施，运行中需保障其可靠性。数字孪生（DT）可用于运行时监控与异常检测，但为TCMS构建DT面临训练数据不足以及高质量时序与上下文特征提取困难。为此，论文提出KDDT：利用语言模型提取上下文特征，LSTM提取时序特征，并通过知识蒸馏利用域外数据扩充训练数据量。在Alstom提供的两个工业数据集上，KDDT的F1分别达0.931和0.915。实证研究还表明，DT模型、语言模型和知识蒸馏分别平均提升F1 12.4%、3%和6.05%。
Cyber-physical systems (CPSs), like train control and management systems (TCMS), are becoming ubiquitous in critical infrastructures. As safety-critical systems, ensuring their dependability during operation is crucial. Digital twins (DTs) have been increasingly studied for this purpose owing to their capability of runtime monitoring and warning, prediction and detection of anomalies, etc. However, constructing a DT for anomaly detection in TCMS necessitates sufficient training data and extracting both chronological and context features with high quality. Hence, in this paper, we propose a novel method named KDDT for TCMS anomaly detection. KDDT harnesses a language model (LM) and a long short-term memory (LSTM) network to extract contexts and chronological features, respectively. To enrich data volume, KDDT benefits from out-of-domain data with knowledge distillation (KD). We evaluated KDDT with two datasets from our industry partner Alstom and obtained the F1 scores of 0.931 and 0.915, respectively, demonstrating the effectiveness of KDDT. We also explored individual contributions of the DT model, LM, and KD to the overall performance of KDDT, via a comprehensive empirical study, and observed average F1 score improvements of 12.4%, 3%, and 6.05%, respectively.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2309-04616,
title = {KDDT: Knowledge Distillation-Empowered Digital Twin for Anomaly Detection},
author = {Qinghua Xu and Shaukat Ali and Tao Yue and Zaimovic Nedim and Inderjeet Singh},
booktitle = {FSE},
year = {2023},
doi = {10.1145/3611643.3613879}
}