Attack Pattern Mining to Discover Hidden Threats to Industrial Control Systems
arXiv:2508.04561 · doi:10.1007/s10207-026-01236-y
Abstract
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.
References in corpus (7)
- SMOTE: Synthetic Minority Over-sampling Technique
- Social physics
- Saving Human Lives: What Complexity Science and Information Systems can Contribute
- Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems
- Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems
- Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems
- Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems