Leveraging Large Language Models (LLMs) for Enhanced Software Security Analysis and Malware Detection
Dr. Kun Sun
KEY INTERESTS
Trustworthy computing environments; Moving target defense; AI/ML security; Smartphone security; Software-defined networking; Software security
AFFILIATIONS/APPOINTMENTS
Professor, Information Sciences and Technology (IST) Department and Department of Computer Science, George Mason University
Associate Director, Center for Secure Information Systems (CSIS), George Mason University
Director, Sun Security Laboratory (Sunlab), George Mason University
ACADEMIC DEGREES
PhD, Computer Science, North Carolina State University
LEVERAGING LARGE LANGUAGE MODELS FOR ENHANCED SOFTWARE SECURITY ANALYSIS AND MALWARE DETECTION
The proliferation of Android apps has led to an increase in potentially harmful software, making efficient and accurate security analysis critical. Current methods rely heavily on human experts, which is time-consuming and limited in scope. Likewise, while machine learning approaches show promise, they often lack explainability, hindering result verification. This project proposes an innovative framework predicated on leveraging LLMs and Retrieval-Augmented Generation (RAG) techniques to enhance software security analysis and malware detection for Android applications.