论文标题
可解释的用于工业控制系统网络安全的异常检测
Explainable Anomaly Detection for Industrial Control System Cybersecurity
论文作者
论文摘要
工业控制系统（ICS）在管理许多重要系统在智能制造中的运行（例如电站，供水系统和制造地点）方面变得越来越重要。尽管大量数字数据可能是系统性能的推动力，但数据安全引起了严重的关注。因此，异常检测对于防止网络安全入侵和系统攻击至关重要。已经提出并实现了许多基于AI的异常检测方法，但是仍然很难解释“黑匣子”。在这项研究中，我们建议使用可解释的人工智能来增强基于LSTM的自动编码器-OCSVM学习模型的视角和可靠的结果，以用于IC中的异常检测。我们根据著名的SCADA数据集证明了我们提出的方法的性能。
Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites. While massive digital data can be a driving force for system performance, data security has raised serious concerns. Anomaly detection, therefore, is essential for preventing network security intrusions and system attacks. Many AI-based anomaly detection methods have been proposed and achieved high detection performance, however, are still a "black box" that is hard to be interpreted. In this study, we suggest using Explainable Artificial Intelligence to enhance the perspective and reliable results of an LSTM-based Autoencoder-OCSVM learning model for anomaly detection in ICS. We demonstrate the performance of our proposed method based on a well-known SCADA dataset.
