Abstract-Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone malwares are currently limited to signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new and unknown malwares creating a window of opportunity for attackers. As smartphones become host for sensitive data and applications, extended malware detection mechanisms are necessary complying with the resource constraints.The contribution of this paper is twofold. First, we perform static analysis on the executables to extract their function calls in Android environment using the command readelf. Function call lists are compared with malware executables for classifying them with PART, Prism and Nearest Neighbor Algorithms. Second, we present a collaborative malware detection approach to extend these results. Corresponding simulation results are presented.

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We present a new technique based on using embedded compass (magnetic) sensor for efficient use of 3D space around a mobile device for interaction with the device. Around Device Interaction (ADI) enables extending interaction space of small mobile and tangible devices beyond their physical boundary. Our proposed method is based on using compass (magnetic field) sensor integrated in new mobile devices (e.g. iPhone 3GS, G1/2 Android). In this method, a properly shaped permanent magnet (e.g. a rod, pen or a ring) is used for interaction. The user makes coarse gestures in 3D space around the device using the magnet. Movement of the magnet affects magnetic field sensed by the compass sensor integrated in the device. The temporal pattern of the gesture is then used as a basis for sending different interaction commands to the mobile device. The proposed method does not impose changes in hardware and physical specifications of the mobile device, and unlike optical methods is not limited by occlusion problems. Therefore, it allows for efficient use of 3D space around device, including back of device. Zooming, turning pages, accepting/rejecting calls, clicking items, controlling a music player, and mobile game interaction are some example use cases. Initial evaluation of our algorithm using a prototype application developed for iPhone shows convincing gesture classification results.

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