2021/09/13 by Marcus Botacin, Botacin, Marcus, André Grégio +1 · 1 citation
Computer Science · #Advanced Malware Detection Techniques #Cryptography and Security (cs.CR) #Digital and Cyber Forensics #FOS: Computer and information sciences #Network Security and Intrusion Detection #Software Engineering Research
paper · pdf · doi:10.48550/arxiv.2109.06127
openalex publication_date 2021/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Malware and other suspicious software often hide behaviors and components behind logic bombs and context-sensitive execution paths. Uncovering these is essential to react against modern threats, but current solutions are not ready to detect these paths in a completely automated manner. To bridge this gap, we propose the Malware Multiverse (MalVerse), a solution able to inspect multiple execution paths via symbolic execution aiming to discover function inputs and returns that trigger malicious behaviors. MalVerse automatically patches the context-sensitive functions with the identified symbolic values to allow the software execution in a traditional sandbox. We implemented MalVerse on top of angr and evaluated it with a set of Linux and Windows evasive samples. We found that MalVerse was able to generate automatic patches for the most common evasion techniques (e.g., ptrace checks).