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

TL;DR

This paper introduces a group lasso regression framework for detecting and localizing cyber-attacks in multistage manufacturing systems, improving detection speed and accuracy over traditional methods.

Contribution

It presents a novel group lasso-based approach specifically designed for attack detection and localization in complex multistage manufacturing systems, addressing propagation challenges.

Findings

Outperforms traditional hypothesis testing in detection delay

Achieves higher localization accuracy

Effective in a linear multistage manufacturing system

Abstract

As Industry 4.0 and digitalization continue to advance, the reliance on information technology increases, making the world more vulnerable to cyber-attacks, especially cyber-physical attacks that can manipulate physical systems and compromise operational data integrity. Detecting cyber-attacks 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 linear multistage manufacturing system.