PSO Supported Ensemble Algorithm for Bad Data Detection Against Intelligent Hacking Algorithm

• Main Authors: Levent Yavuz, Ahmet Soran, Ahmet Onen, SM Muyeen
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-07-01
• Series: Frontiers in Energy Research
• Subjects: bad data detection; hacking mechanism; k-nearest neighbor; linear discriminant analysis; logistic regression; machine learning
• Online Access: https://www.frontiersin.org/articles/10.3389/fenrg.2021.649460/full

Power system cybersecurity has recently become important due to cyber-attacks. Due to advanced computer science and machine learning (ML) applications being used by malicious attackers, cybersecurity is becoming crucial to creating sustainable, reliable, efficient, and well-protected cyber-systems. Power system operators are needed to develop sophisticated detection mechanisms. In this study, a novel machine-learning-based detection algorithm that combines the five most popular ML algorithms with Particle Swarm Optimizer (PSO) is developed and tested by using an intelligent hacking algorithm that is specially developed to measure the effectiveness of this study. The hacking algorithm provides three different types of injections: random, continuous random, and slow injections by adaptive manner. This would make detection harder. Results shows that recall values with the proposed algorithm for each different type of attack have been increased.