Attack Pattern Mining to Discover Hidden Threats to Industrial Control Systems
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Abstract (arXiv)
This work focuses on validation of attack pattern mining in the context of Industrial Control System (ICS) security. A comprehensive security assessment of an ICS requires generating a large and variety of attack patterns. For this purpose we have proposed a data driven technique to generate attack patterns for an ICS. The proposed technique has been used to generate over 100,000 attack patterns from data gathered from an operational water treatment plant. In this work we present a detailed case study to validate the attack patterns.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 6 Aug 2025.
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References · 12
- 2018IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detectioncited by 42
- 2021Social physicscited by 14
- 2019Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systemscited by 8
- 2020Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systemscited by 8
- 2021Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systemscited by 6
- 2021Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learningcited by 5
- 2014Saving Human Lives: What Complexity Science and Information Systems can Contributecited by 4
- 2017A Deep Learning-based Framework for Conducting Stealthy Attacks in Industrial Control Systemscited by 4
- 2021Scanning the Cycle: Timing-based Authentication on PLCscited by 4
- 2025AttackLLM: LLM-based Attack Pattern Generation for an Industrial Control Systemcited by 4
- 2019Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systemscited by 3
- 2025Adversarial Sample Generation for Anomaly Detection in Industrial Control Systemscited by 1
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