# Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems

arXiv: https://arxiv.org/abs/2004.05005 · HTML: https://mlsanity.com/paper/2004.05005/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification](https://mlsanity.com/paper/2301.13122/) (2023)
- [SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection](https://mlsanity.com/paper/2308.06819/) (2023)
- [Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective](https://mlsanity.com/paper/2404.10796/) (2024)
- [Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities](https://mlsanity.com/paper/2101.12097/) (2021)
- [Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability](https://mlsanity.com/paper/2505.01328/) (2025)
- [Attacks Against Mobility Prediction in 5G Networks](https://mlsanity.com/paper/2402.19319/) (2024)
- [Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attacks](https://mlsanity.com/paper/2409.18244/) (2024)
- [Rigid Body Adversarial Attacks](https://mlsanity.com/paper/2502.05669/) (2025)
- [Adapting Noise-Driven PUF and AI for Secure WBG ICS: A Proof-of-Concept Study](https://mlsanity.com/paper/2510.22283/) (2025)
- [Towards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection](https://mlsanity.com/paper/2609.07244/) (2026)

## Abstract (arXiv)

The proliferation and application of machine learning based Intrusion Detection Systems (IDS) have allowed for more flexibility and efficiency in the automated detection of cyber attacks in Industrial Control Systems (ICS). However, the introduction of such IDSs has also created an additional attack vector; the learning models may also be subject to cyber attacks, otherwise referred to as Adversarial Machine Learning (AML). Such attacks may have severe consequences in ICS systems, as adversaries could potentially bypass the IDS. This could lead to delayed attack detection which may result in infrastructure damages, financial loss, and even loss of life. This paper explores how adversarial learning can be used to target supervised models by generating adversarial samples using the Jacobian-based Saliency Map attack and exploring classification behaviours. The analysis also includes the exploration of how such samples can support the robustness of supervised models using adversarial training. An authentic power system dataset was used to support the experiments presented herein. Overall, the classification performance of two widely used classifiers, Random Forest and J48, decreased by 16 and 20 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks.