Chinese Journal of Network and Information Security ›› 2018, Vol. 4 ›› Issue (1): 45-51.doi: 10.11959/j.issn.2096-109x.2018011

• Papers • Previous Articles     Next Articles

Behavior authentication of Web users based on machine learning

Zenan WU1,Liqin TIAN1,2(),Zhigang WANG2   

  1. 1 School of Computer Science and Technology,North China Institute of Science and Technology,Beijing 101601,China
    2 School of Computer Science and Technology,Qinghai Normal University,Xining 810008,China
  • Revised:2018-01-05 Online:2018-01-01 Published:2018-02-09
  • Supported by:
    The National Natural Science Foundation of China(61472137);The Central University Basic Research Fees Fund Project(3142015022);The Central University Basic Research Fees Fund Project(3142017053);The Key Research and Development Projects of Qinghai Province(2016-SF-130);The Key Research and Development Projects of Qinghai Province(2017-ZJ-752);Internet of Things Monitoring Engineering Research Center of Hebei Province(3142016020);Key Laboratory of Internet of Things of Qinghai Province(2017-ZJ-Y21)

Abstract:

According to the security problem of Web user information,the user behavior was analyzed and authenticated by the method of machine learning.First of all,through the principal component analysis to reduce the dimension of the original data set,then use the SVM algorithm to allow the computer to learn the history of user behavior evidence,to get a model to identify the user's identity.The practical application and theoretical analysis show that the model in user behavior identification authentication,can be more accurate and efficient classification of dangerous users and trusted users,analysis lay a solid theoretical and practical basis for the high performance user behavior such as electronic commerce,network finance and other key of Internet applications.

Key words: information security, user behavior authentication, support vector machine, principal component analysis

CLC Number: 

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