Cybersecurity Advances in SCADA Systems

Abstract

The management of critical infrastructure heavily relies on Supervisory Control and Data Acquisition [SCADA] systems, but as they become more connected, insider attacks become a greater concern. Insider threat detection systems [IDS] powered by machine learning have emerged as a potential answer to this problem. In order to identify and neutralize insider threats, this review paper examines the most recent developments in machine learning algorithms for insider IDS in SCADA security systems. A thorough analysis of research articles published in 2019 and later, focussed on variety of machine learning methods, have been adopted in this review study to better highlight difficulties and challenges being faced by professionals, and how the study will contribute to overcome them. The results show that, in addition to conventional methods, machine-learning based intrusion detection techniques offer important advantages in identifying complex and covert insider attacks. Finding pertinent insider threat data for model training and guaranteeing data privacy and security are still difficult to address. Ensemble techniques and hybrid strategies show potential for improving detection resiliency. In conclusion, machine learning-based insider IDS has the potential to protect critical infrastructures by strengthening SCADA systems against insider attacks. The similarities and differences between cyber physical systems and SCADA systems, emphasizing security challenges and the potential for mutual improvement were also reviewed in this study. In order to be as effective as possible, future research should concentrate on addressing issues with data collecting and privacy, investigating the latest developments in technology, and creating hybrid models. SCADA systems can accomplish proactive and effective defence against insider attacks by integrating machine learning advancements, maintaining their dependability and security in the face of emerging threats.