Abstract

The increasing reliance on digital technologies within the power sector has introduced considerable cybersecurity risks, especially from malware and trojans. These threats can disrupt essential operations, manipulate grid functions, and compromise the integrity of energy systems, thereby endangering both economic stability and national security. This research aims to create a detection framework tailored to the specific challenges of the power sector. The proposed framework utilizes advanced methods such as behaviour based anomaly detection, machine learning algorithms, and both static and dynamic analysis of software. By examining distinct patterns and signatures associated with malware and trojans targeting power sector software, this study seeks to enhance early detection capabilities and response strategies. Real-world case studies and simulations will be employed to evaluate the effectiveness of these detection techniques, highlighting the necessity of robust and adaptable security measures to protect critical energy infrastructure.