Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (1): 287-292.doi: 10.11896/jsjkx.181102118

• Information Security • Previous Articles     Next Articles

Classification and Evaluation of Mobile Application Network Behavior Based on Deep Forest and CWGAN-GP

JIANG Peng-fei,WEI Song-jie   

  1. (School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)
  • Received:2018-11-16 Published:2020-01-20
  • About author:JIANG Peng-fei,born in 1995,postgra-duate,is not member of China Computer Federation (CCF).His main research interests include traffic analysis and deep learning;WEI Song-jie,born in 1977,Ph.D,professor,is member of China Computer Federation (CCF).His main research interests include network security,network data analysis and monitoring,abnormal event detection and simulation.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61472189),CERNET Innovation Project (NGII20160105).

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Abstract: In view of the problems that the large number and complex functions of mobile applications,and mixed with a variety of malicious applications,this paper analyzed the network behavior of applications for Android platform,and designed reasonable network behavior trigger events for different types of applications to simulate network interaction behavior.Based on the network event behavior sequence,the improved deep forest model is used to classify and identify applications.The optimal classification accuracy can reach 99.03%,and it has high accuracy,high recall rate,high F1-Score and low training time.In addition,in order to solve the problems of limited number of application samples and high time cost of data acquisition,a data enhancement method using CWGAN-GP was proposed.Compared with the original generative adversarial network,the training of the model is more stable,and the data of specified categories can be generated by only one training.The experimental results show that the classification accuracy is improved by about 9% after joining the generated data to train the deep forest model together.

Key words: Application classification, Deep forest, Generative adversarial network, Network behavior, Traffic classification

CLC Number: 

  • P309
[1]GHORBANZADEH M,CHEN Y,MA Z M,et al.A neural network approach to category validation of Android applications[C]∥2013 International Conference on Computing,Networking and Communications (ICNC).San Diego:IEEE,2013:740-744.
[2]HAO H K,LI Z J,YU H B.An effective approach to measuring and assessing the risk of android application[C]∥2015 International Symposium on Theoretical Aspects of Software Enginee-ring.Nanjing,China:IEEE,2015:31-38.
[3]WANG R,FENG D G,YANG Y,et al.Semantics-Based Malware Behavior Signature Extraction and Detection Method[J]. Journal of Software,2012,23(2):378-393.
[4]WEI S J,YANG L.Android Malware Characterization Based on Static Analysis of Hierarchical API Usage[J].Computer Science,2015,42(1):155-158.
[5]CHUANG H Y,WANG S D.Machine learning based hybrid behavior models for Android malware analysis[C]∥2015 IEEE International Conference on Software Quality,Reliability and Security.Vancouver:IEEE,2015:201-206.
[6]MINN S,FU S,LV T.Algorithm for exact recovery of Bayesian network for classification[J].Application Research of Compu-ters,2016,33(5):1327-1334.
[7]ESTE A,GRINGOLI F,SALGARELLI L.Support vector machines for TCP traffic classification[J].Computer Networks,2009,53(14):2476-2490.
[8]ZANDER S,NGUYEN T,ARMITAGE G.Automated traffic classification and application identification using machine lear-ning[C]∥The IEEE Conference on Local Computer Networks 30th Anniversary (LCN’05) l.Sydney:IEEE,2005:250-257.
[9]ZHOU Z H,FENG J.Deep forest:Towards an alternative to deep neural networks[J].arXiv:1702.08835,2017.
[10]WANG J Y,XU M K,WANG H Y,et al.Automated Detection of the Inconsistence between App Behavior and Privacy Policy of Android Apps[J].Journal of Frontiers of Computer Science and Technology,2019,13(1):56-69.
[11]ZHOU Z H.Ensemble methods:foundations and algorithms
[M].CRC Press,2012.
[12]CHEN T Q,GUESTRIN C.Xgboost:A scalable tree boosting system[C]∥Proceedings of the 22nd Acmsigkdd International Conference on Knowledge Discovery and Data Mining.New York:ACM,2016:785-794.
[13]WANG K F,GOU C,DUAN Y J,et al.Generative Adversarial Networks:The State of the Art and Beyond [J].Acta Automatica Sinica,2017,43(3):321-332.
[14]ARJOVSKY M,CHINTALA S,BOTTOU L.Wasserstein gan[J].arXiv:1701.07875,2017.
[15]GULRAJANI I,AHMED F,ARJOVSKY M,et al.Improved training of wasserstein gans[C]∥Advances in Neural Information Processing Systems.Long Beach,USA:Neural Informaton Processing Sysmtes,2017:5767-5777.
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