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- W4207037802 abstract "AbstractThe electric power grid is a complex critical infrastructure that forms the lifeline of modern society. Securing networked Cyber-Physical Systems (CPSs), such as the power grid critical infrastructure, has emerged as paramount importance to national security and economic security. Adversaries exploit the vulnerabilities in both cyber- and physical-domains and deploy stealthy multistage cyberattacks to disrupt reliable operation of the smart grid. Recent cyberattacks (in 2015 and 2016) that targeted the Ukrainian power grid and targeted cyberattack campaign during 2017–19 demonstrate the need for techniques to detect and mitigate such attacks. These incidents demonstrated growing threats and vulnerabilities within the smart grid, where critical control centers present a major attack target and whose compromise could result in major impacts on grid operation, including blackouts. This chapter presents cyberattack surface analysis for CPS wide-area control system and proposes an attack-resilient framework for supervisory control and data acquisition, energy management system and Wide-Area Monitoring, Protection and Control (WAMPAC) applications of bulk power grid. We discuss Machine Learning (ML) based anomaly detection and mitigation for a WAMPAC application, namely Wide-Area Damping Control (WADC) as case study. We present the architecture of the Hardware-in-the-Loop (HIL) CPS security power grid testbed and discuss the experimental results for the case study." @default.
- W4207037802 created "2022-01-26" @default.
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- W4207037802 date "2022-01-01" @default.
- W4207037802 modified "2023-09-27" @default.
- W4207037802 title "Machine Learning-Based and Physics-Based Attack Resilient Wide-Area Monitoring, Protection and Control Systems" @default.
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- W4207037802 doi "https://doi.org/10.1007/978-3-030-83236-0_8" @default.
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