ADT: Time series anomaly detection for cyber-physical systems via deep reinforcement learning
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Cyber-physical systems (CPS) play a vital role in modern society across various sectors, ranging from smart grid to water treatment, and their security has become one of the major concerns. Due to the significantly growing complexity and scale of CPS and cyber-attacks, it is imperative to develop defense and prevention strategies specifically for CPS that are adaptive, scalable, and robust. An important research and application direction in this domain is time series anomaly detection within CPS utilizing advanced machine learning techniques, such as deep learning and reinforcement learning. However, many anomaly detectors fail to balance between detection performance and computational overhead, limiting their applicability in CPS. In this paper, we introduce a novel agent-based dynamic thresholding (ADT) method based on the deep reinforcement learning technique, i.e. deep Q-network (DQN), to model thresholding in anomaly detection as a Markov decision process. By utilizing anomaly scores generated from an autoencoder and other useful information perceived from a simulated environment, ADT performs the optimal dynamic thresholding control, facilitating real-time adaptive anomaly detection for time series. Rigorous evaluations were conducted on realistic datasets from water treatment and industrial control systems, specifically SWaT, WADI, and HAI, comparing against established benchmarks. The experimental results demonstrate ADT's superior detection performance, dynamic thresholding capability, data-efficient learning, and robustness. Notably, ADT, even when trained on minimal labeled data, consistently outperforms benchmarks with F1 scores ranging from 0.995 to 0.999 across all datasets. It is effective even in challenging scenarios where the environmental feedback is noisy, delayed, or partial. Beyond its direct application as an advanced anomaly detector, ADT possesses the versatility to act as a lightweight dynamic thresholding controller, boosting other anomaly detection models. This underscores ADT's considerable promise in sophisticated and dynamic CPS environments.
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In contrast to other data types that mainly target offline detection accuracy, simulation data are primarily used to learn adaptive defense policies (e.g., dynamic threshold control, attack-defense strategy selection, policy-based mitigation) in MDP or game-theoretic environments.
“First, in contrast to other data types that mainly target offline detection accuracy, simulation data are primarily used to learn adaptive defense policies such as dynamic threshold control, attack-defense strategy selection, and policy-based mitigation in MDP or game-theoretic environments [119-121].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [120]
The reviewed simulation-data studies show two representative ways of using simulation: cyber-range environments (e.g., CyberBattleSim) where the entire attack-defense process is simulated, and Markov game settings that reuse real ICS/CPS datasets while generating perturbed trajectories to evaluate robustness against unseen attacks.
“Second, the reviewed studies show two representative ways of using simulation: cyber-range environments such as CyberBattleSim, where the entire attack-defense process is simulated [119], and Markov game settings that reuse real ICS/CPS datasets while generating perturbed trajectories to evaluate robustness against unseen attacks [120,121].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [120]
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- Authors
- Yang, X., Howley, E., & Schukat, M.
- Affiliations
- Ollscoil na Gaillimhe – University of Galway (IE)
- Corresponding author
- Howley, Enda
- Schukat, Michael
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 141
- Pages
- 103825
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W4393253409
- Cited by
- 47
- References
- 62
- Citations by year
- 2026: 22 · 2025: 21 · 2024: 4
- FWCI
- 6.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers & Security (publishedVersion, cc-by)
- Funders
- University of Galway
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- time series anomaly detection, cyber-physical systems, deep reinforcement learning, deep Q-network, dynamic thresholding, Markov decision process, water treatment systems, industrial control systems, SWaT dataset, WADI dataset
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