Detecting the One-Shot Dummy Attack on the Power Industrial Control Processes With an Unsupervised Data-Driven Approach

Dear Editor, Dummy attack (DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this letter, targeting a more practical case, we aim to detect the one-shot DA, with the purpose of revealing the DA once it is launched. Specifically, we first formulate an optimization problem to generate one-shot DAs. Then, an unsupervised data-driven approach based on a modified local outlier factor (MLOF) is proposed to detect them. To improve the detection performance, the measurements are preprocessed with the gamma transformation and the power patterns are extracted from historical data and integrated into the MLOF algorithm. Finally, extensive experiments are conducted to evaluate the performance of the proposed approach with real-world load data.

Automated summary

In this letter, a detection method is proposed for the one-shot dummy attack (DA), a deep and stealthy data integrity attack that hides corrupted measurements in power industrial control processes. The method formulates an optimization problem to generate one-shot DAs, and then proposes an unsupervised data-driven approach based on a modified local outlier factor (MLOF) to detect them. Experimental results on real-world load data demonstrate the effectiveness of the proposed approach.