Adversarial Retraining for Machine Learning-Based Man-in-the-Middle Attack Detection in Smart Grid Industrial Control Systems

Summary

Researchers evaluated several widely used machine learning models for detecting Man-in-the-Middle attacks in smart grid industrial control systems, using the RESLab smart grid dataset. Fast-gradient sign method perturbations reduced Random Forest detection accuracy by 17.48 percentage points for binary classification and 14.86 percentage points for multiclass classification. Retraining the Random Forest classifier with adversarial samples raised accuracy under fixed-source FGSM attacks to 81.59% (binary) and 81.31% (multiclass) while preserving performance on clean data.

Mitigation

Adversarial retraining, which incorporates hard-to-detect adversarial samples into the model's retraining process.

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