# Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations

**Authors:** Muhammad Azmi Umer, Khurum Nazir Junejo, Muhammad Taha Jilani, Aditya P. Mathur  
**arXiv:** [2202.11917](https://arxiv.org/abs/2202.11917)  
**Date:** 2022-02-24  
**Summary page:** https://www.summarizepaper.com/en/paper/2202.11917v1/

## Key points

- Machine learning methods are increasingly used to develop resilient Industrial Control Systems (ICS) against cyber-attacks.
- Two key areas of focus for these methods are network-level intrusion detection and physical process anomaly detection.
- Four types of machine learning methods are employed: supervised, semi-supervised, unsupervised, and reinforcement learning.
- The survey examines relevant studies in the public domain and organizes them within a 7-dimensional space for comparison.
- The target audience includes researchers, students, and practitioners interested in securing ICS using machine learning techniques.
- Challenges associated with implementing these methods are identified, along with research gaps that need further investigation.
- Recommendations are proposed to address these research gaps and enhance the effectiveness of intrusion and anomaly detection in ICS using machine learning.

## Summary

Methods from machine learning are being increasingly utilized to develop Industrial Control Systems (ICS) that are resilient to cyber-attacks. These methods primarily focus on two key areas: detecting intrusions at the network level by analyzing information obtained from network packets, and identifying anomalies at the physical process level by analyzing data that represents the system's physical behavior. This comprehensive survey delves into four types of machine learning methods employed for intrusion and anomaly detection in ICS: supervised, semi-supervised, unsupervised, and reinforcement learning. The survey extensively examines literature available in the public domain, meticulously selecting and analyzing relevant studies. To facilitate comparison, the selected literature is organized within a 7-dimensional space. The target audience for this survey includes researchers, students, and practitioners interested in understanding the application of machine learning techniques in securing ICS. Throughout the survey, various challenges associated with implementing these machine learning methods are identified as well as research gaps which are highlighted to provide insights into areas where further investigation is needed. Based on these findings, recommendations are proposed to address these research gaps and enhance the effectiveness of intrusion and anomaly detection in ICS using machine learning. Overall, this refined summary emphasizes the growing significance of applying machine learning approaches to design robust ICS capable of withstanding cyber-attacks. It underscores the importance of both network-level intrusion detection and physical process anomaly detection while providing a comprehensive overview of different types of machine learning methods used in this context. Additionally, it identifies challenges and research gaps which serve as a valuable resource for future studies aiming to improve upon existing techniques and fill knowledge voids in this field.