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

Abstract

Abstract As Industry 4.0 and digitization continue to advance, the reliance on information technology increases, making the world more vulnerable to cyberattacks, especially cyber-physical attacks that can manipulate physical systems and compromise sensor data integrity. Detecting cyberattacks in multistage manufacturing systems (MMS) is crucial due to the growing sophistication of attacks and the complexity of MMS. Attacks can propagate throughout the system, affecting subsequent stages and making detection more challenging than in single-stage systems. Localization is also critical due to the complex interactions in MMS. To address these challenges, a group lasso regression-based framework is proposed to detect and localize attacks in MMS. The proposed algorithm outperforms traditional hypothesis testing-based methods in expected detection delay and localization accuracy, as demonstrated in a simple linear multistage manufacturing system.