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. Although some security hardening solutions have been proposed to secure them, traditional security measures such as firewalls, intrusion detection systems, and access controls are not enough to provide adequate protection against modern cyber threats. Therefore, there is a need for novel security hardening solutions that can detect and respond to emerging, previously unknown threats. In parallel with this, 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. Network traffic data collected from various sources are analyzed to identify anomalies that may indicate the presence of cyber threats. Various machine learning algorithms are used to analyze the data. 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.