Malicious Code Detection Using LLM
Al Amin Hossain, Mithun Kumar PK, Junjie Zhang, Fathi Amsaad · 2024
The rapid evolution of cyber threats raises the inevitability of the advancement of innovative and effective approaches in cybersecurity. There are numerous cyber threats; among these threats, malicious code, including viruses, worms, and sophisticated malware, poses significant risks to digital systems. The existing threat detection methods often rely on signature-based techniques and need help to keep pace with the dynamic and evolving characteristics of malware. Large language models (LLMs) such as GPT-4, renowned for their ability to perform natural language processing, offer a promising alternative for enhancing malicious code detection. This research paper proposes a novel approach using large language models (LLMs) to detect unwanted malicious code in Java source code, leveraging the Mixtral architecture. The Mixtral model is trained on a diverse dataset of benign and malicious Java code, enabling it to learn complex patterns and characteristics of malicious code. Experimental results validate the efficiency of the proposed in identifying malicious code, outperforming existing static analysis tools.