Project demo in CMRI
Using statistic features in web traffic packets, classic machine learning algorithms（KNN，SVM，RandomForest）as training datasets; Output the result that tell where the unknown traffic packets come from; To make it simple, 10 APP's datasource files(*.pcap) were used; The demo system covers the application of 2-label classifiers and multiple(6-label) classification problems. The General simplified-test result is awesome：(still need to be improved)
1.Test result:(6)multiple-labels precision above 94% for randomforest,about 50% for KNN and SVM
2.Test result:2-labels precision above 90% for KNN,SVM,RF etc.
  Author ： Xiaobo Ma， graduation project (implementation of paper :: Research on mobile application identification technology based on network traffic statistics) ，Xi'an JiaoTong University
  References：
Wang Q, Yahyavi A, Kemme B, et al. I know what you did on your smartphone: Inferring app usage over encrypted data traffic[C]// IEEE Conference on Communications and Network Security. IEEE, 2015:433-441.
Taylor V F, Spolaor R, Conti M, et al. AppScanner: Automatic Fingerprinting of Smartphone Apps from Encrypted Network Traffic[J]. 2016:439-454.
Conti M, Mancini L V, Spolaor R, et al. Can't You Hear Me Knocking: Identification of User Actions on Android Apps via Traffic Analysis[C]// ACM Conference on Data and Application Security and Privacy. ACM, 2014:297-304.
  etc.