# Cybersecurity for Industrial Control Systems: A Survey

arXiv: https://arxiv.org/abs/2002.04124 · HTML: https://mlsanity.com/paper/2002.04124/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations](https://mlsanity.com/paper/2202.11917/) (2022)
- [Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents](https://mlsanity.com/paper/2602.02164/) (2026)
- [Machine learning on knowledge graphs for context-aware security monitoring](https://mlsanity.com/paper/2105.08741/) (2021)
- [Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques](https://mlsanity.com/paper/2502.13256/) (2025)
- [Security of Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2212.06123/) (2022)
- [Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development](https://mlsanity.com/paper/2502.14080/) (2025)
- [HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2211.00277/) (2022)
- [Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges](https://mlsanity.com/paper/2410.13894/) (2024)
- [Environment-Grounded Multi-Agent Workflow for Autonomous Penetration Testing](https://mlsanity.com/paper/2603.24221/) (2026)
- [APIOT: Autonomous Vulnerability Management Across Bare-Metal Industrial OT Networks](https://mlsanity.com/paper/2605.02346/) (2026)
- [NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing](https://mlsanity.com/paper/2609.00604/) (2026)
- [Towards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection](https://mlsanity.com/paper/2609.07244/) (2026)

## Abstract (arXiv)

Industrial Control System (ICS) is a general term that includes supervisory control & data acquisition (SCADA) systems, distributed control systems (DCS), and other control system configurations such as programmable logic controllers (PLC). ICSs are often found in the industrial sectors and critical infrastructures, such as nuclear and thermal plants, water treatment facilities, power generation, heavy industries, and distribution systems. Though ICSs were kept isolated from the Internet for so long, significant achievable business benefits are driving a convergence between ICSs and the Internet as well as information technology (IT) environments, such as cloud computing. As a result, ICSs have been exposed to the attack vectors used in the majority of cyber-attacks. However, ICS devices are inherently much less secure against such advanced attack scenarios. A compromise to ICS can lead to enormous physical damage and danger to human lives. In this work, we have a close look at the shift of the ICS from stand-alone systems to cloud-based environments. Then we discuss the major works, from industry and academia towards the development of the secure ICSs, especially applicability of the machine learning techniques for the ICS cyber-security. The work may help to address the challenges of securing industrial processes, particularly while migrating them to the cloud environments.