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It is well-known that Android malware constantly evolves so as to evadedetection. This causes the entire malware population to be non-stationary.Contrary to this fact, most of the prior works on Machine Learning basedAndroid malware detection have assumed that the distribution of the observedmalware characteristics (i.e., features) does not change over time. In thiswork, we address the problem of malware population drift and propose a novelonline learning based framework to detect malware, named CASANDRA(Contextaware, Adaptive and Scalable ANDRoid mAlware detector). In order toperform accurate detection, a novel graph kernel that facilitates capturingapps' security-sensitive behaviors along with their context information fromdependency graphs is proposed. Besides being accurate and scalable, CASANDRAhas specific advantages: i) being adaptive to the evolution in malware featuresover time ii) explaining the significant features that led to an app'sclassification as being malicious or benign. In a large-scale comparativeanalysis, CASANDRA outperforms two state-of-the-art techniques on a benchmarkdataset achieving 99.23% F-measure. When evaluated with more than 87,000 appscollected in-the-wild, CASANDRA achieves 89.92% accuracy, outperformingexisting techniques by more than 25% in their typical batch learning settingand more than 7% when they are continuously retained, while maintainingcomparable efficiency.

Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)