Lightweight Long Short-Term Memory Variational Auto-Encoder for Multivariate Time Series Anomaly Detection in Industrial Control Systems

• Main Authors: Damer, N. (Author), Fährmann, D. (Author), Kirchbuchner, F. (Author), Kuijper, A. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: anomaly detection; Anomaly detection; Auto encoders; Brain; Cyber security; Cyber-attacks; Encoder architecture; Industrial control systems; Long short-term memory; Multivariate time series; pattern recognition; Pattern recognition; Physical world; security; Security; System behaviors; Water supply systems; Water treatment
• Online Access: View Fulltext in Publisher

 Heterogeneous cyberattacks against industrial control systems (ICSs) have had a strong impact on the physical world in recent decades. Connecting devices to the internet enables new attack surfaces for attackers. The intrusion of ICSs, such as the manipulation of industrial sensory or actuator data, can be the cause for anomalous ICS behaviors. This poses a threat to the infrastructure that is critical for the operation of a modern city. Nowadays, the best techniques for detecting anomalies in ICSs are based on machine learning and, more recently, deep learning. Cybersecurity in ICSs is still an emerging field, and industrial datasets that can be used to develop anomaly detection techniques are rare. In this paper, we propose an unsupervised deep learning methodology for anomaly detection in ICSs, specifically, a lightweight long short-term memory variational auto-encoder (LW-LSTM-VAE) architecture. We successfully demonstrate our solution under two ICS applications, namely, water purification and water distribution plants. Our proposed method proves to be efficient in detecting anomalies in these applications and improves upon reconstruction-based anomaly detection methods presented in previous work. For example, we successfully detected 82.16% of the anomalies in the scenario of the widely used Secure Water Treatment (SWaT) benchmark. The deep learning architecture we propose has the added advantage of being extremely lightweight. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.