Mac-A-Mal: macOS malware analysis framework resistant to anti evasion techniques

Abstract

With macOS increasing popularity, the number, and variety of macOS malware are rising as well. Yet, very few tools exist for dynamic analysis of macOS malware. In this paper, we propose a macOS malware analysis framework called Mac-A-Mal. We develop a kernel extension to monitor malware behavior and mitigate several anti-evasion techniques used in the wild. Our framework exploits the macOS features of XPC service invocation that typically escape traditional mechanisms for detection of children processes. Performance benchmarks show that our system is comparable with professional tools and able to withstand VM detection. By using Mac-A-Mal, we discovered 71 unknown adware samples (8 of them using valid distribution certificates), 2 keyloggers, and 1 previously unseen trojan involved in the APT32 OceanLotus.