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In this paper a novel system for detecting meaningful deviations in a mobileapplication's network behavior is proposed. The main goal of the proposedsystem is to protect mobile device users and cellular infrastructure companiesfrom malicious applications. The new system is capable of: (1) identifyingmalicious attacks or masquerading applications installed on a mobile device,and (2) identifying republishing of popular applications injected with amalicious code. The detection is performed based on the application's networktraffic patterns only. For each application two types of models are learned.The first model, local, represents the personal traffic pattern for each userusing an application and is learned on the device. The second model,collaborative, represents traffic patterns of numerous users using anapplication and is learned on the system server. Machine-learning methods areused for learning and detection purposes. This paper focuses on methodsutilized for local (i.e., on mobile device) learning and detection ofdeviations from the normal application's behavior. These methods wereimplemented and evaluated on Android devices. The evaluation experimentsdemonstrate that: (1) various applications have specific network trafficpatterns and certain application categories can be distinguishable by theirnetwork patterns, (2) different levels of deviations from normal behavior canbe detected accurately, and (3) local learning is feasible and has a lowperformance overhead on mobile devices.

Detection of Deviations in Mobile Applications Network Behavior