With the increased use of ICS(Industrial control systems )cybersecurity has become a major concern. One of the challenges of securing ICS is that many ICS are built on legacy systems. These old systems were not designed with cybersecurity in mind, making them vulnerable to modern cyber threats. One way to protect ICSs against cyberattacks is by using advanced intrusion detection techniques that rely on machine learning algorithms. In this research, we explore how well different types of machine learning algorithms can be used to create effective intrusion detection systems. Additionally, we investigate to what extent the algorithms can distinguish different attack types.

His research domain is Cybersecurity of Industrial Control Systems (ICSs) and designing Intrusion detection systems (IDSs) using AI&ML. Cyber attacks take the system from its normal function and leave traces on the system state variable or network data. Therefore, advanced AI algorithms such as convolutional neural networks and time series AI could help IDS find cyber attacks using remained data traces from attacks.