# Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning

arXiv: https://arxiv.org/abs/2107.05127 · HTML: https://mlsanity.com/paper/2107.05127/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 11 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection](https://mlsanity.com/paper/1809.02077/) (2018)
- [Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems](https://mlsanity.com/paper/1911.04278/) (2019)
- [Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems](https://mlsanity.com/paper/2012.15740/) (2020)
- [Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems](https://mlsanity.com/paper/2105.10707/) (2021)
- [Scanning the Cycle: Timing-based Authentication on PLCs](https://mlsanity.com/paper/2102.08985/) (2021)
- [Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems](https://mlsanity.com/paper/1907.07487/) (2019)

## Built on by (most cited first, 5 of 5)

- [Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations](https://mlsanity.com/paper/2202.11917/) (2022)
- [AttackLLM: LLM-based Attack Pattern Generation for an Industrial Control System](https://mlsanity.com/paper/2504.04187/) (2025)
- [Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems](https://mlsanity.com/paper/2505.03120/) (2025)
- [A Data-Centric Approach to Generate Invariants for a Smart Grid Using Machine Learning](https://mlsanity.com/paper/2202.06717/) (2022)
- [Attack Pattern Mining to Discover Hidden Threats to Industrial Control Systems](https://mlsanity.com/paper/2508.04561/) (2025)

## Abstract (arXiv)

Adversarial learning is used to test the robustness of machine learning algorithms under attack and create attacks that deceive the anomaly detection methods in Industrial Control System (ICS). Given that security assessment of an ICS demands that an exhaustive set of possible attack patterns is studied, in this work, we propose an association rule mining-based attack generation technique. The technique has been implemented using data from a secure Water Treatment plant. The proposed technique was able to generate more than 300,000 attack patterns constituting a vast majority of new attack vectors which were not seen before. Automatically generated attacks improve our understanding of the potential attacks and enable the design of robust attack detection techniques.