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It is well-known that Android malware constantly evolves so as to evade detection. This causes the entire malware population to be non-stationary. Contrary to this fact, most of the prior works on Machine Learning based Android malware detection have assumed that the distribution of the observed malware characteristics (i.e., features) does not change over time.In this work, we address the problem of malware population drift and propose a novel online learning based framework to detect malware, named CASANDRA (Context-aware, Adaptive and Scalable ANDRoid mAlware detector). In order to perform accurate detection, a novel graph kernel that facilitates capturing apps' security-sensitive behaviors along with their context information from dependency graphs is proposed. Besides being accurate and scalable, CASANDRA has specific advantages: (i) being adaptive to the evolution in malware features over time (ii) explaining the significant features that led to an app's classification as being malicious or benign. In a large-scale comparative analysis, CASANDRA outperforms two state-of-the-art techniques on a benchmark dataset achieving 99.23% F-measure. When evaluated with more than 87,000 apps collected in-the-wild, CASANDRA achieves 89.92% accuracy, outperforming existing techniques by more than 25% in their typical batch learning setting and more than 7% when they are continuously retained, while maintaining comparable efficiency.

Existing Android malware detection approaches use a variety of features such as securitysensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view ) of apps' behaviors with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterize several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevents them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localization. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder.MKLDroid uses a graph kernel to capture structural and contextual information from apps' dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid's unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Malicious code localization caters several important applications such as supporting human analysts studying malware behaviors, engineering malware signatures, and other counter-measures. Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localization experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94% average recall. Our work opens up two new avenues in malware research: (i) enables the research community to elegantly look at Android malware behaviors in multiple perspectives simultaneously, and (ii) performing precise and scalable malicious code localization.