Guest Editorial: Privacy and Security in Smart Grids

Abstract

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Smart grids are complex and extensive critical infrastructures and they are under constant threat from a wide range of attacks, including data theft, false data injection, denial of service attacks, malware attacks, energy theft, as well as coordinated and distributed versions of these attacks
While traditional model-based detection techniques have been proven inadequate in identifying stealthy type attacks, the authors propose a machinelearning based detection framework by combining a support vector regression load predictor with a support vector machine attack detector, which is demonstrated to be effective in detecting both random and intelligently designed load redistribution attacks