More Semantics More Robust: Improving Android Malware Classifiers

Automatic malware classifiers often perform badly on the detection of new malware, i.e., their robustness is poor. We study the machine-learning-based mobile malware classifiers and reveal one reason: the input features used by these classifiers can't capture general behavioural patterns of malware instances. We extract the best-performing syntax-based features like permissions and API calls, and some semantics-based features like happen-befores and unwanted behaviours, and train classifiers using popular supervised and semi-supervised learning methods. By comparing their classification performance on industrial datasets collected across several years, we demonstrate that using semantics-based features can dramatically improve robustness of malware classifiers.