Enhancing android malware sandboxes with anti-evasion code patching

Abstract

Sophisticated Android malware families often implement techniques aimed at avoiding detection. Split personality malware for example, behaves benignly when it detects that it is running on an analysis environment such as a malware sandbox, and maliciously when running on a real user's device. These kind of techniques are problematic for malware analysts, often rendering them unable to detect or understand the malicious behaviour. This is where sandbox hardening comes into play. In this work, we exploit sandbox detection heuristic prediction to proactively generate bytecode patches, in order to disable the malware's ability to detect a malware sandbox. Through the development of AndroNeo, we demonstrate the feasibility of this approach by showing that the heuristic prediction basis is a solid starting point to build upon, and demonstrating that when heuristic prediction is followed by bytecode patch generation, split personality can be defeated. The AndroNeo prototype implements checks at the Java level for API method calls that can distinguish real devices from emulators. The robustness of AndroNeo was demonstrated by showing its ability to identify and patch evasion heuristics within packed code. The relevance of packed malware was confirmed by demonstrating the prevalence of packers in modern day malware samples.