Intelligent Security Hardening of SCADA Systems Using Machine Learning Algorithms

Supervisory Control and Data Acquisition (SCADA) systems play a key role in various industrial processes. Due to their key role in critical infrastructures, in recent years, they have become the target of cyber attackers. In this research, we propose an intelligent security hardening approach for SCADA systems using machine learning algorithms. The proposed approach relies on the collection and analysis of network traffic data from SCADA systems, followed by the application of machine learning algorithms to detect and respond to cyber threats. The proposed approach can improve the security of SCADA systems and reduce the risk of downtime and financial losses due to cyber attacks. It is a more cost-effective security solution compared to traditional security measures.