Ali Taghavirashidizadeh - Department of Electrical and Electronics Engineering, Islamic Azad University, CentralTehran Branch (IAUCTB)

As the world becomes increasingly interconnected, the reliance on Supervisory Control and Data Acquisition (SCADA) systems continues to grow. These systems are vital for monitoring and controlling critical infrastructures such as power plants, water treatment facilities, and manufacturing processes. However, with this increased connectivity comes an elevated risk of cyber threats and attacks. "SCADA Security Threats: For Machine Learning Engineers" is a comprehensive guide that aims to educate machine learning engineers about the unique security challenges faced by SCADA systems. The book provides a deep understanding of the potential vulnerabilities and threats that can compromise the integrity, availability, and confidentiality of these critical systems. The authors explore the intricacies of SCADA systems, including their architecture, protocols, and communication networks. They delve into the various threat vectors, attack techniques, and common vulnerabilities that adversaries exploit to compromise SCADA systems. Furthermore, the book examines the role of machine learning in enhancing SCADA security, discussing the potential applications of anomaly detection, intrusion detection, and threat intelligence.

SCADA, security threats, machine learning engineers, cyber threats, vulnerabilities, attack techniques, anomaly detection, intrusion detection, threat intelligence, architecture, protocols, communication networks, case studies, hands-on exercises, collaboration, cybersecurity, SCADA systems.