RobotDroid: A Lightweight Malware Detection Framework On Smartphones

Abstract

Smartphones have been widely used in recent years due to their capabilities of communication and multimedia processing, thus they also become attack targets of malware. Threat of malicious software has become an important factor in the safety of smartphones. Android is the most popular open-source smartphone operating system and its permission declaration access control mechanisms can’t detect the behavior of malware. In this paper, a new software behavior signature based malware detection framework named RobotDroid using SVM active learning algorithm is proposed, active learning algorithm is very efficient in solving a small amount of labeled samples and unlabeled samples posed a lot of mixed sample training set classify problems, as a result, RobotDroid can detect kinds of malicious software and there variants effective ly in runtime and it can self extend malware characteristics database dynamically. E xperimental results show that the approach has high detection rate and low rate of false