Effective detection of mobile malware behavior based on explainable deep neural network

The rapid growth of the number of new mobile malware variants has posed a severe threat to user's property and privacy. Recent studies show that deep neural networks can detect malicious traffic with high accuracy. However, a deep neural network works like a black box in the sense that its structure doesn't give any insight on how it works. To overcome this drawback, we propose a method to extract rules from a deep neural network and then use the extracted rules to detect malicious network traffic. Specifically, for a trained deep neural network, we first construct one input-hidden tree per each hidden layer to represent the rules extracted between the input of the neural network and the output of that hidden layer. Then we construct one hidden-output tree to represent the rules extracted between the outputs of all hidden layers and the output of the neural network. Finally, these trees are merged to form one rule tree using the outputs of the hidden layers as a bridge. We have performed extensive experiments to verify the effectiveness of our method in terms of accuracy, precision, recall and F-Measure metrics by comparing it with other state-of-the-art methods. Experimental results show that our method achieves high accuracy using the packet size of only the first nine packets as a feature, which also gives good interpretability on how the deep neural network performs to detect malicious traffic. Besides, we design an online detection system based on FPGA to provide online detection in a high-speed network environment using rule tree, which reduces the difficulty of embedding a deep neural network into FPGA. (C) 2020 Published by Elsevier B.V.