The Abnormal Mobile Malware Analysis Based on Behavior Categorization

Abstract

With the explosive growth of mobile malware in mobile internet, many polymorphic and metamorphic mobile malware appears and causes difficulty of detection. A mobile malware network behavior data mining method based on behavior categorization is proposed to detect the behavior of new or metamorphic mobile malware. The network behavior is divided into different categories after analyzing the behavior character of mobile malware and those different behavior data of known malware and normal action are used to train the Naïve Bayesian classifier respectively. Those Naïve Bayesian classifiers are used to detect the mobile malware network behavior. The experiment result shows that Behavior Categorization based Naïve Bayesian Classifier (BCNBC) can improve the detection accuracy and it can meet the requirement of real time process in mobile internet.