SEEAD: A Semantic-Based Approach for Automatic Binary Code De-obfuscation

Increasingly sophisticated code obfuscation techniques are quickly adopted by malware developers to escape from malware detection and to thwart the reverse engineering effort of security analysts. State-of-the-art de-obfuscation approaches rely on dynamic analysis, but face the challenge of low code coverage as not all software execution paths and behavior will be exposed at specific profiling runs. As a result, these approaches often fail to discover hidden malicious patterns. This paper introduces SEEAD, a novel and generic semantic-based de-obfuscation system. When building SEEAD, we try to rely on as few assumptions about the structure of the obfuscation tool as possible, so that the system can keep pace with the fast evolving code obfuscation techniques. To increase the code coverage, SEEAD dynamically directs the target program to execute different paths across different runs. This dynamic profiling scheme is rife with taint and control dependence analysis to reduce the search overhead, and a carefully designed protection scheme to bring the program to an error free status should any error happens during dynamic profile runs. As a result, the increased code coverage enables us to uncover hidden malicious behaviors that are not detected by traditional dynamic analysis based de-obfuscation approaches. We evaluate SEEAD on a range of benign and malicious obfuscated programs. Our experimental results show that SEEAD is able to successfully recover the original logic from obfuscated binaries.