How to Verify Users via Web Behavior Features: Based on the Human Behavioral Theory
Nowadays, the Internet has penetrated into people’s daily life in many ways, and people usually use the form of web account to carry out web activities. However, the problem of user account being illegally occupied by others has become increasingly prominent, and the security of user account is not guaranteed. On the other side, the network’s advantage of recording data easily provides a valuable opportunity to comprehensively record a tremendous amount of human behaviors. Based upon the status and problems, we present an approach to distinguish genuine account owners from intruders. This study constructs a series of web behavior features based on web usage logs, and uses machine learning to identify users based on Bayesian theorem and structural risk minimization criterion. Experiments show that web behavior analysis is a feasible way for user verification.
KeywordsHuman behavior Human web behavior User verification Machine learning
This work was supported by Fundamental Research Funds for the Central Universities NO. 106112016CDJXY110003, 2016.1-2017.12 and the National Natural Science Foundation of China under Grant No. 71671020.
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