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As Industry 4.0 and digitalization continue to advance, the reliance oninformation technology increases, making the world more vulnerable tocyber-attacks, especially cyber-physical attacks that can manipulate physicalsystems and compromise operational data integrity. Detecting cyber-attacks inmultistage manufacturing systems (MMS) is crucial due to the growingsophistication of attacks and the complexity of MMS. Attacks can propagatethroughout the system, affecting subsequent stages and making detection morechallenging than in single-stage systems. Localization is also critical due tothe complex interactions in MMS. To address these challenges, a group lassoregression-based framework is proposed to detect and localize attacks in MMS.The proposed algorithm outperforms traditional hypothesis testing-based methodsin expected detection delay and localization accuracy, as demonstrated in alinear multistage manufacturing system.

GLHAD: A Group Lasso-based Hybrid Attack Detection and Localization Framework for Multistage Manufacturing Systems