Smart grid technology has transformed electricity distribution and management, but it also exposes critical infrastructures to cybersecurity threats. To mitigate these risks, the integration of machine learning (ML) and natural language processing (NLP) techniques has emerged as a promising approach. This survey paper analyses current research and applications related to ML and NLP integration, exploring methods for risk assessment, log analysis, threat analysis, intrusion detection, and anomaly detection. It also explores challenges, potential opportunities, and future research directions for enhancing smart grid cybersecurity through the synergy of ML and NLP. The study's key contributions include providing a thorough understanding of state-of-the-art techniques and paving the way for more robust and resilient smart grid defences against cyber threats.

Anomaly Detection Autoencoders Integration ML NLP Sentiment Analysis Smart Grid