Mobile malware traffic detection approach based on value-derivative GRU - Hanxun ZHOU; Chen CHEN; Runze FENG; Junkun XIONG; Hong PAN; Wei GUO (Tongxin xuebao)
For the dramatic increase in the number and variety of mobile malware had created enormous challenge for information security of mobile network users,a value-derivative GRU-based mobile malware traffic detection approach was proposed in order to solve the problem that it was difficult for a RNN-based mobile malware traffic detection approach to capture the dynamic changes and critical information of abnormal network traffic.The low-order and high-order dynamic change information of the malicious network traffic could be described by the value-derivative GRU approach at the same time by introducing the concept of “accumulated state change”.In addition,a pooling layer could ensure that the algorithm can capture key information of malicious traffic.Finally,simulation were performed to verify the effect of accumulated state changes,hidden layers,and pooling layers on the performance of the value-derivative GRU algorithm.Experiments show that the mobile malware traffic detection approach based on value-derivative GRU has high detection accuracy.