Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection
This addresses the challenge of acquiring sufficient and diverse training data for malware detection in cybersecurity, particularly for institutions like banks and governments dealing with targeted threats, though it is an incremental improvement over existing methods.
The paper tackles the problem of limited training data for ML-driven malware detection by introducing MARVOLO, a programmatic data augmentation tool that uses semantics-preserving code transformations to generate augmented malware and benign datasets, resulting in accuracy boosts of up to 5%.
Data augmentation has been rare in the cyber security domain due to technical difficulties in altering data in a manner that is semantically consistent with the original data. This shortfall is particularly onerous given the unique difficulty of acquiring benign and malicious training data that runs into copyright restrictions, and that institutions like banks and governments receive targeted malware that will never exist in large quantities. We present MARVOLO, a binary mutator that programmatically grows malware (and benign) datasets in a manner that boosts the accuracy of ML-driven malware detectors. MARVOLO employs semantics-preserving code transformations that mimic the alterations that malware authors and defensive benign developers routinely make in practice , allowing us to generate meaningful augmented data. Crucially, semantics-preserving transformations also enable MARVOLO to safely propagate labels from original to newly-generated data samples without mandating expensive reverse engineering of binaries. Further, MARVOLO embeds several key optimizations that keep costs low for practitioners by maximizing the density of diverse data samples generated within a given time (or resource) budget. Experiments using wide-ranging commercial malware datasets and a recent ML-driven malware detector show that MARVOLO boosts accuracies by up to 5%, while operating on only a small fraction (15%) of the potential input binaries.