Penetrating the Power Grid: Realistic Adversarial Attacks on Smart Grid Intrusion Detection Systrems

Intrusion Detection System, Adversarial Attacks, Critical Infrastructure, Machine Learning, Smart Grids.

The widespread adoption and use of Machine Learning-Based Intrusion Detection Systems (ML-IDS) has increased the flexibility and efficiency of automated cyber attack detection in smart grid systems. However, the introduction of such IDSes has created a new attack vector against the learning models commonly known as adversarial attacks. Such attacks could have serious consequences in smart grid systems, as adversaries can evade detection by the IDS. This could lead to delayed attack detection. From the existing literature, a lot of research propose threat models that are inappropriate for generating realistic adversarial attacks. In this research, we model realistic adversarial attacks with a focus on real attacker capabilities and circumstances required by attackers to launch feasible and successful adversarial attacks. We demonstrate how adversarial learning can be used to target ML models by using the Fast Gradient Sign Method (FGSM) and Jacobian-based Saliency Map Attack (JSMA). A power system dataset generated from smart grid testbed was used for testing the models. Overall, the classification performance of three widely used classifiers Random Forest, XGBoost and Naive Bayes decreased when adversarial samples were present. The outcomes of this paper are useful for helping researcher model on realistic scenarios to avoid dealing with hypothetical problems.

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