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Abstract-The rapid growing trend of mobile devices continues to soar causing massive increase in cyber security threats. Most pervasive threats include ransom-ware, banking malware, premium SMS fraud. The solitary hackers use tailored techniques to avoid detection by the traditional antivirus. The emerging need is to detect these threats by any flow-based network solution. Therefore, we propose and evaluate a network based model which uses ensemble Machine Learning (ML) methods in order to identify the mobile threats, by analyzing the network flows of the malware communication. The ensemble ML methods not only protect over-fitting of the model but also cope with the issues related to the changing behavior of the attackers. The focus of this study is on android based mobile malwares due to its popularity among users. We have used ensemble methods to combine output of 5 supervised ML algorithms such as RF, PART, JRIP, J.48 and Ridor. Based on the evaluation results, the proposed model was found efficient at detecting known and unknown threats with the accuracy of 98.2%.

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Mobile malware attacks increased three folds in the past few years and continued to expand with the growing number of mobile users. Adversary uses a variety of evasion techniques to avoid detection by traditional systems, which increase the diversity of malicious applications. Thus, there is a need for an intelligent system that copes with this issue. This paper proposes a machine learning (ML) based framework to counter rapid evolution of mobile threats. This model is based on flow-based features, that will work on the network side. This model is designed with adversarial input in mind. The model uses 40 timebased network flow features, extracted from the real-time traffic of malicious and benign applications. The proposed model not only to detects the known and unknown mobile threats but also deals with the changing behavior of the attackers by triggering the retraining phase. The proposed framework can be used by the mobile operators to protect their subscribers. We used several supervised ML algorithms to build the model and got an average accuracy of up to 99.8%.