AI

Advanced Dynamic and Static Malware Analysis using LLMs

Mon, May 12 2025

Malware Analysis AI LLM Cyber Security

The increasing complexity of malware highlights the need for advanced analysis tools, both static and dynamic, for effective reverse engineering and behavioral analysis of a given sample. While static methods such as disassembly and code review remain crucial, many malware samples use packers and obfuscation techniques that necessitate memory captures and dynamic analysis [Dynamic, 2012]. Similarly, hooking system and API calls at lower levels provides a more comprehensive view of a program’s true behavior. It enables analysts to capture transient execution stages in a multi-layered malware

Aznaur Aliev

M.S. Student

AI Deep learning Computer Vision Reinforcement Learning

Mhd. Emad Alolaby

Associate Editor, King Abdullah University of Science and Technology

Network Design telecommunication 5G networks GSM Digital Communications optical fiber AI deep learning MATLAB

Mohammed A Shukri

Consultant (former), King Abdullah University of Science and Technology

Neural Networks AI Python BLE communication

Technical Consultant at KAUST (King Abdullah University of Science and Technology).