Application Identification under Multi-Service Integration Platform

Multi-service integration platform (MIP) is becoming a new way for mobile applications to provide services, such as the ChatBot of Facebook and the applet of WeChat. However, currently there are no special means and filtering strategies to supervise the services running on various MIPs. Existing solutions for program detection and traffic analysis are not suitable for MIP scenarios, which creates favorable conditions for the dissemination of illegal content through MIP. To address this issue, in this work we propose a new approach to identify mobile applications running on MIP platforms. The proposed approach uses IP flow to reconstruct data units of both transport and ap-plication layers respectively. By this way, we can capture the data transmission behavior of multi-protocol layers and obtain richer semantic features for application identification. Then, multi-kernel convolutional neural networks (CNN s) and long short term memory (LSTM) neural networks are employed to extract and aggregate the multi-scale features from the perspective of both protocol layer and time series. Finally, the fused features generated by the models are used to identify the category of the pending applications by a classifier composed of a fully connected neural network. We validate the proposed approach by three real datasets. The experimental results show that the proposed approach outperforms most existing benchmark methods in performance.