Enhancing Cybersecurity Through AI-Driven Intrusion Detection Systems in Industrial Control Systems

Alikhan Bekzhanov; Aizada Sadykova; Yerzhan Mukhamedi

doi:10.62951/ijies.v1i2.91

Authors

Alikhan Bekzhanov l-Farabi Kazakh National University
Aizada Sadykova l-Farabi Kazakh National University
Yerzhan Mukhamedi l-Farabi Kazakh National University

DOI:

https://doi.org/10.62951/ijies.v1i2.91

Keywords:

Cybersecurity, Industrial Control Systems, Intrusion Detection System, anomaly detection, machine learning

Abstract

Industrial Control Systems (ICS) play a critical role in managing infrastructure but are vulnerable to cyber-attacks. This paper presents an AI-driven Intrusion Detection System (IDS) specifically designed for ICS, utilizing a combination of supervised and unsupervised machine learning algorithms. By incorporating real-time anomaly detection and pattern recognition, the proposed IDS identifies potential intrusions while maintaining high accuracy. The experimental results show the system’s effectiveness in detecting cyber threats in real-world ICS environments, providing a scalable solution for enhancing cybersecurity in critical infrastructure.

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