LUNA: Quantifying and Leveraging Uncertainty in Android Malware Analysis through Bayesian Machine Learning

Abstract

Android's growing popularity seems to be hindered only by the amount of malware surfacing for this open platform. Machine learning algorithms have been successfully used for detecting the rapidly growing number of malware families appearing on a daily basis. Existing solutions along these lines, however, have a common limitation: they are all based on classical statistical inference and thus ignore the concept of uncertainty invariably involved in any prediction task. In this paper, we show that ignoring this uncertainty leads to incorrect classification of both benign and malicious apps. To reduce these errors, we utilize Bayesian machine learning – an alternative paradigm based on Bayesian statistical inference – which preserves the concept of uncertainty in all steps of calculation. We move from a black-box to a white-box approach to identify the effects different features (such as sensitive resource usage, declared activities, services and intent filters etc.) have on the classification status of an app. We show that incorporating uncertainty in the learning pipeline helps to reduce incorrect decisions, and significantly improves the accuracy of classification. We achieve a false positive rate of 0.2% compared to the previous best of 1%. We present sufficient details to allow the reader to reproduce our results through openly available probabilistic programming tools and to extend our techniques well beyond the boundaries of this paper.