MobiSentry: Towards Easy and Effective Detection of Android Malware on Smartphones

Bingfei Ren, Chuanchang Liu, Bo Cheng, Jie Guo, Junliang Chen
<span title="2018-11-21">2018</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/khqygtfojnby3jyh5obuwd7ko4" style="color: black;">Mobile Information Systems</a> </i> &nbsp;
Android platform is increasingly targeted by attackers due to its popularity and openness. Traditional defenses to malware are largely reliant on expert analysis to design the discriminative features manually, which are easy to bypass with the use of sophisticated detection avoidance techniques. Therefore, more effective and easy-to-use approaches for detection of Android malware are in demand. In this paper, we present MobiSentry, a novel lightweight defense system for malware classification
more &raquo; ... d categorization on smartphones. Besides conventional static features such as permissions and API calls, MobiSentry also employs the N-gram features of operation codes (n-opcode). We present two comprehensive performance comparisons among several state-of-the-art classification algorithms with multiple evaluation metrics: (1) malware detection on 184,486 benign applications and 21,306 malware samples, and (2) malware categorization on DREBIN, the largest labeled Android malware datasets. We utilize the ensemble of these supervised classifiers to design MobiSentry, which outperforms several related approaches and gives a satisfying performance in the evaluation. Furthermore, we integrate MobiSentry with Android OS that enables smartphones with Android to extract features and to predict whether the application is benign or malicious. Experimental results on real smartphones show that users can easily and effectively protect their devices against malware through this system with a small run-time overhead.
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