Revolutionizing Malware Detection: Feature-Based Approach for Targeting Diverse Malware Categories

• Published in: 2023 IEEE International Carnahan Conference on Security Technology (ICCST) pp. 1 - 5
• Main Authors: Jain, Sanyam, Thaseen, Sumaiya
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.10.2023
• Subjects: Feature extraction; Malicious file detection; Malware; Malware Bazaar; Multi-Class Classification; Principal component analysis; Security; Strain; Task analysis; WEKA tool
• ISSN: 2153-0742
• DOI: 10.1109/ICCST59048.2023.10474249

In today's digital landscape, the task of identifying various types of malicious files has become progressively challenging. Modern malware exhibits increasing sophistication, often evading conventional anti-malware solutions. The scarcity of data on distinct and novel malware strains further complicates effective detection. In response, this research presents an innovative approach to malware detection, specifically targeting multiple distinct categories of malicious software. In the initial stage, Principal Component Analysis (PCA) is performed and achieved a remarkable accuracy rate of 95.39%. Our methodology revolves around leveraging features commonly accessible from user-uploaded files, aligning with the contextual behavior of typical users seeking to identify malignancy. This underscores the efficacy of the unique feature-based detection strategy and its potential to enhance contemporary malware identification methodologies. The outcomes achieved attest to the significance of addressing emerging malware threats through inventive analytical paradigms.