Abstract. Malware analysis is critical for malware detection and prevention. To defeat malware analysis and detection, today malware commonly adopts various sophisticated anti-detection techniques, such as performing debugger, emulator, and virtual machine fingerprinting, and camouflaging its traffic as normal legitimate traffic. These mechanisms produce more and more stealthy malware that greatly challenges existing malware analysis schemes. In this work, targeting application level stealthy malware, we propose Malyzer, the key of which is to defeat malware anti-detection mechanisms at startup and runtime so that malware behavior during execution can be accurately captured and distinguished. For analysis, Malyzer always starts a copy, referred to as a shadow process, of any suspicious process on the same host by defeating all startup anti-detection mechanisms employed in the process. To defeat internal runtime anti-detection attempts, Malyzer further makes this shadow process mutually invisible to the original suspicious process. To defeat external anti-detection attempts, Malyzer makes as if the shadow process runs on a different machine to the outside. Since ultimately malware will conduct local information harvesting or dispersion, Malyzer constantly monitors the shadow process's behavior and adopts a hybrid scheme for its behavior analysis. In our experiments, Malyzer can accurately detect all malware samples that employ various anti-detection techniques.
al., REF proposed Malyzer, using the shadow process, which monitored other processes running malicious code.
Malyzer: Defeating Anti-detection for Application-Level Malware Analysis
ACNS
Computer Science