Behavior Classification based Self-learning Mobile Malware Detection

Abstract

More and more mobile malware appears on mobile internet and pose great threat to mobile users. It is difficult for traditional signature-based anti-malware system to detect the polymorphic and metamorphic mobile malware. A mobile malware behavior analysis method based on behavior classification and self-learning data mining is proposed to detect the malicious network behavior of the unknown or metamorphic mobile malware. A network behavior classification module is used to divide the network behavior data of mobile malware into different categories according to the behavior characteristic in the training and detection phase. Three types of network behavior data of mobile malware and normal network access are employed to train the different Naive Bayesian classifier respectively. Those classifiers are used to analyze the corresponding type of network behavior to detect the new or metamorphic mobile malware. An incremental self- learning method is adopted to gradually optimize those Naive Bayesian Classifiers for different behavior. The simulation results showed that those Naive Bayesian Classifiers based on behavior classification have better accuracy rate of analysis on mobile malware network behavior. Performance simulation results showed that the network behavior analysis system based on the proposed method can analyze the mobile malware on mobile internet in real time.