Abstract
The chapter delves into an innovative approach to improve the security of smart grid systems through signal processing techniques. The chapter begins with an insightful introduction, highlighting the pressing need for robust attack detection mechanisms in the evolving landscape of smart grids. It then unfolds a multi-level attack detection strategy, emphasizing the importance of a comprehensive defense framework. Singular Spectral Analysis (SSA) emerges as a key player, and its application in attack detection is thoroughly explored. Further, the focus extends to multivariate SSA for control center-level detection, showcasing extensions in both training and detection phases. The chapter meticulously evaluates the performance of the detection algorithm, with a dedicated section on performance enhancement strategies. The heart of this chapter lies in presenting real-world results of multi-level attack detection, including at the RTU/IED and control center levels. Hypothesis testing-based attack detection, particularly SSA Hoeffding Test, takes the stage, accompanied by adaptive threshold selection techniques. The results of adaptive attack detection are dissected, including performance under load variations, comparisons with existing strategies, and scalability evaluations.
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Sreejith, A., Shanti Swarup, K. (2024). Signal Processing-Based Attack Detection. In: Cyber-Security for Smart Grid Control. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-97-1302-8_6
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DOI: https://doi.org/10.1007/978-981-97-1302-8_6
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