Titlebook: Cyber-Security for Smart Grid Control; Vulnerability Assess Amulya Sreejith,K. Shanti Swarup Book 2024 The Editor(s) (if applicable) and Th

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MITRE ATT&CK for?Smart Grid Cyber-Securityher extended to other power system and industrial control system applications for vulnerability assessment and penetration testing. Overall, this chapter serves as a comprehensive guide for security practitioners, researchers, and stakeholders seeking to fortify power systems against cyber threats,

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Signal Processing-Based Attack Detectionevel 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 performanc

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Attack Mitigation and Recovery in Smart Grid Controlpter delves into the innovative realm of IoT-based hardware models for enhancing attack mitigation and recovery capabilities. We describe the method to build a simple IoT model that can be used to launch attacks and implement detection methods using Kali Linux, Raspberry Pi and Python programming. T

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Attack Modeling for?Smart Grid Controlrious attack scenarios, and their potential impacts. It serves as a critical resource for researchers, practitioners, and policymakers seeking to enhance the resilience and security of smart grid systems. The chapter commences with an introduction, setting the stage for an in-depth exploration of th

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Vulnerability Assessment for?Multi-area Load Frequency Controlntroduction, the chapter proceeds to delve into key methodologies and models designed to evaluate and enhance grid resilience. The chapter introduces the concept of Data Penetration Testing, shedding light on its importance in gauging the robustness of grid systems. It then addresses the Cascading O

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Signal Processing-Based Attack Detectionwith 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)

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Machine Learning-Based Attack Detections. 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. Simulat