Paper Key : IRJ************691
Author: A.sai Lochan
Date Published: 13 Nov 2024
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.Malware, short for "malicious software," is any code or program created with the intent to harm, disrupt, or gain unauthorized access to computer systems. Detecting whether software is infected with malware is crucial due to the rising frequency of attacks, which threaten businesses through data breaches and operational interruptions. Malware can severely impair systems by reducing performance, corrupting data, or encrypting large amounts of information on a device. This emphasizes the need to minimize false positives during the detection process to prevent unnecessary disruptions. The study proposes an adaptable framework based on machine learning, which has shown significant potential in accurately identifying malicious software. Although traditional antivirus programs offer strong protection, the evolving nature of cyber threats demands continuous updates to malware databases. These repositories store historical malware data and are essential for predicting new behaviour and enabling faster, more effective responses to emerging threats.