A Novel Approach of Evasive Malware Analysis Through Binary Opcode and BERT

Abstract

This study addressed the growing challenge of evasive malware detection by introducing an innovative approach that integrates binary opcode analysis with the advanced machine learning capabilities of BERT (Bidirectional Encoder Representations from Transformers). Focusing on the rapidly evolving landscape of cybersecurity, this study explored the intricacies of malware behavior, particularly in the context of opcode obfuscation and direct OS lower-level API access. Through a comprehensive methodology, the study demonstrated the effectiveness of combining traditional disassembly techniques with state-of-the-art deep learning algorithms. The findings revealed a significant distinction between malware and benign software in terms of their opcode structures and system interactions. This study not only offered a novel perspective in malware analysis, but also set the stage for future advancements in cybersecurity tools and techniques. The integration of binary opcode analysis and BERT represented a vital development in cybersecurity, providing enhanced detection capabilities and a deeper understanding of sophisticated malware threats.