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

arXiv: https://arxiv.org/abs/2306.07993 · HTML: https://mlsanity.com/paper/2306.07993/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Trustworthy AI](https://mlsanity.com/paper/2011.02272/) (2020)
- [Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach](https://mlsanity.com/paper/1809.05258/) (2018)
- [Smart Grid Cyber Attacks Detection using Supervised Learning and Heuristic Feature Selection](https://mlsanity.com/paper/1907.03313/) (2019)
- [Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems](https://mlsanity.com/paper/2002.08567/) (2020)
- [Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network](https://mlsanity.com/paper/2210.06649/) (2022)
- [An Explainable Artificial Intelligence Framework for Quality-Aware IoE Service Delivery](https://mlsanity.com/paper/2201.10822/) (2022)
- [Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging](https://mlsanity.com/paper/2108.01466/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Grid](https://mlsanity.com/paper/2403.06388/) (2024)

## Abstract (arXiv)

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.