Behavior Classification based Self-learning Mobile Malware Detection

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.