Behavior authentication of Web users based on machine learning
| Autor: | WU Zenan, TIAN Liqin, WANG Zhigang |
|---|---|
| Jazyk: | English<br />Chinese |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | 网络与信息安全学报, Vol 4, Iss 1, Pp 45-51 (2018) |
| Druh dokumentu: | article |
| ISSN: | 2096-109x 2096-109X |
| DOI: | 10.11959/j.issn.2096-109x.2018011 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|