SUNY Poly Assistant Professor Dr. Mahmoud Badr and peers have published new research in Applied Sciences titled "XAI-Based Accurate Anomaly Detector That Is Robust Against Black-Box Evasion Attacks for the Smart Grid." This significant research introduces an advanced anomaly detection system specifically designed to protect smart grid networks from sophisticated cyber threats, which is a notable advancement in the use of XAI in cybersecurity for critical infrastructure.

By combining accurate anomaly detection with resilience against sophisticated attacks, this XAI-based approach could help safeguard future smart grids, promoting reliable and secure energy distribution systems in increasingly digitalized environments.

Traditional cybersecurity models often struggle against black-box evasion attacks, where attackers manipulate data inputs to bypass detection mechanisms. This research leverages explainable artificial intelligence (XAI) to enhance the transparency and robustness of the anomaly detection model, allowing for both high accuracy in identifying malicious activities and resilience against evasion attempts.

In smart grid environments, where the infrastructure and network communication must remain secure, accurate anomaly detection is critical. The XAI-based model in this study provides clear reasoning for its detection decisions, making it easier for operators to understand and trust the system's outputs. This transparency is a key differentiator from conventional "black-box" machine learning models, which often leave users in the dark about why specific alerts are triggered.

More information: Islam Elgarhy et al, XAI-Based Accurate Anomaly Detector That Is Robust Against Black-Box Evasion Attacks for the Smart Grid, Applied Sciences (2024). DOI: 10.3390/app14219897