Malware Reverse Engineering with Large Language Model for Superior Code Comprehensibility and IoC Recommendations

Abstract Malware reverse engineering, the process of dissecting malicious software to understand its functionality and behavior, faces significant challenges due to the complexity and obfuscation techniques employed by modern malware. The application of Gemini Pro for interpreting reverse-engineered malware code introduces a novel and significant approach to enhancing the understanding of complex malware behaviors. By leveraging advanced natural language processing capabilities, the model provides detailed and accurate explanations of malware's functional components, offering substantial improvements over traditional analysis methods. The study demonstrates the model's proficiency in identifying key operational mechanisms and recommending relevant indicators of compromise, which are crucial for effective threat detection and mitigation. A comprehensive comparative analysis reveals that Gemini Pro outperforms conventional static and dynamic analysis tools in terms of clarity, coherence, and time efficiency. Detailed case studies of various malware samples, including Ramnit, Kelihos, and Lollipop, illustrate the model's ability to generate clear and actionable insights, thereby facilitating better decision-making in cybersecurity contexts. The findings underscore the potential of integrating advanced natural language processing models into cybersecurity workflows to significantly enhance the efficiency and effectiveness of malware analysis and mitigation efforts.