Autonomous Threat Detection in Smart Grids

Abstract

Smart grids have become an important part of modern energy infrastructure due to their ability to improve efficiency, reliability, and sustainability. These systems use technologies such as IoT devices, cloud computing, sensors, and real-time communication networks to manage electricity distribution effectively. However, the increasing digitalization of smart grids has also increased their vulnerability to cyberattacks such as malware, ransomware, denial-of-service attacks, and false data injection attacks.
Autonomous Threat Detection (ATD) systems powered by Artificial Intelligence (AI) and Machine Learning (ML) provide effective solutions for detecting and preventing cyber threats in real time. AI-based techniques such as anomaly detection, deep learning, and predictive analytics help identify suspicious activities and improve cybersecurity performance. Machine learning and deep learning models including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are widely used for detecting complex attack patterns.
This study also discusses challenges such as data privacy, scalability, and response latency in smart grid environments. The findings indicate that AI-driven autonomous threat detection systems can improve grid security, reduce detection time, and enhance the reliability and resilience of modern smart grids.