Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid

TL;DR

This paper presents a trustworthy AI framework for proactive detection and explanation of cyber attacks in smart grids, enhancing security and trustworthiness through dynamic risk quantification and interpretability.

Contribution

It introduces a novel AI framework that combines attack detection, root cause analysis, and risk quantification specifically tailored for smart grid cybersecurity.

Findings

Framework effectively detects cyber threats in smart grids.

Provides transparent explanations of attack causes.

Quantifies attack risk dynamically using proposed methods.

Abstract

The rapid growth of distributed energy resources (DERs), such as renewable energy sources, generators, consumers, and prosumers in the smart grid infrastructure, poses significant cybersecurity and trust challenges to the grid controller. Consequently, it is crucial to identify adversarial tactics and measure the strength of the attacker's DER. To enable a trustworthy smart grid controller, this work investigates a trustworthy artificial intelligence (AI) mechanism for proactive identification and explanation of the cyber risk caused by the control/status message of DERs. Thus, proposing and developing a trustworthy AI framework to facilitate the deployment of any AI algorithms for detecting potential cyber threats and analyzing root causes based on Shapley value interpretation while dynamically quantifying the risk of an attack based on Ward's minimum variance formula. The experiment with a state-of-the-art dataset establishes the proposed framework as a trustworthy AI by fulfilling the capabilities of reliability, fairness, explainability, transparency, reproducibility, and accountability.