Machine Learning-Based Attack Detection

This chapter presents an innovative approach to enhance the cyber-security of smart grid systems through the utilization of machine learning techniques. It commences with a comprehensive introduction and then delves into the pivotal role of machine learning in the context of smart grid attack detection. A focal point emerges in the form of Support Vector Data Description (SVDD) for online attack detection, elucidating its core components. Simulating the application of SVDD, the chapter meticulously details the results and engages in insightful discussions. Furthermore, a comparative analysis with other classifiers is presented, shedding light on the strengths of the SVDD approach. In summary, this chapter offers a comprehensive exploration of machine learning-based attack detection in smart grids, featuring practical simulation results and discussions. It underscores the effectiveness and adaptability of the SVDD methodology while providing valuable insights into its application in real-world scenarios.