Crowdroid: behavior-based malware detection system for Android
Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices
Tap-Wave-Rub: lightweight malware prevention for smartphones using intuitive human gestures
Proceedings of the sixth ACM conference on Security and privacy in wireless and mobile networks
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This paper presents a distributed Support Vector Machine (SVM) algorithm in order to detect malicious software (malware) on a network of mobile devices. The light-weight system monitors mobile user activity in a distributed and privacy-preserving way using a statistical classification model which is evolved by training with examples of both normal usage patterns and unusual behavior. The system is evaluated using the MIT reality mining data set. The results indicate that the distributed learning system trains quickly and performs reliably. Moreover, it is robust against failures of individual components.