Enhanced Security of Industrial Fault Diagnosis Systems With Synergy of Covert Attacks and Low-Overhead Defenses

Industrial fault diagnosis systems(IFDS) play a crucial role in industrial systems but are susceptible to cyberattacks with data manipulation. Existing security methods are costly and ineffective for stealthy attacks. To address these challenges, we propose a security framework that synergizes covert attacks and low-overhead defenses in this article. The framework unifies a fault diagnostic feature-based adversarial attack (FDFA) strategy and a certified bound-based representative sampling (CBRS) one. With the FDFA, adversarial examples are created to deceive the monitor in an IFDS. The CBRS strategy filters the representative examples by computing the certified bounds of the clean examples, and then generates adversarial examples that approximate the decision bounds. FDFA-CBRS balances adversarial robustness and standard accuracy while reducing computational cost. Extensive experiments on public datasets demonstrate FDFA-CBRS's advantage in improving the robustness of IFDS confrontation, maintaining the standardized accuracy, and reducing the computational cost.