Trustworthy Artificial Intelligence Framework for Proactive Detection
  and Risk Explanation of Cyber Attacks in Smart Grid
Md. Shirajum Munir Sachin Shetty Danda B. Rawat
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.