McBoost: Boosting Scalability in Malware Collection and Analysis Using Statistical Classification of Executables

In this work, we propose Malware Collection Booster (McBoost), a fast statistical malware detection tool that is intended to improve the scalability of existing malware collection and analysis approaches. Given a large collection of binaries that may contain both hitherto unknown malware and benign executables, McBoost reduces the overall time of analysis by classifying and filtering out the least suspicious binaries and passing only the most suspicious ones to a detailed binary analysis process for signature extraction. The McBoost framework consists of a classifier specialized in detecting whether an executable is packed or not, a universal unpacker based on dynamic binary analysis, and a classifier specialized in distinguishing between malicious or benign code. We developed a proof-of-concept version of McBoost and evaluated it on 5,586 malware and 2,258 benign programs. McBoost has at) accuracy of 873%, and an A rea Under the ROC curve (AUC) equal to 0.977. Our evaluation also shows that McBoost reduces the overall time of analysis to only a fraction (e.g., 13.4%) of the computation time that would otherwise be required to analyze large sets of mixed malicious and benign executables.