Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities

In many colleges and universities, MOOCs have been applied in many courses, including ideological and political course, which is very important for college students’ ideological and moral education. Ideological and political MOOCs break the limitations of time and space, and students can convenientl...

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Bibliographic Details
Published in:Mobile information systems 2021-04, Vol.2021, p.1-9
Main Authors: Hong, Ni, Wang, Xuefeng, Wang, Zhonghua
Format: Article
Language:English
Subjects:
Algorithms
Behavior
Colleges & universities
Communication
Computer networks
Correlation analysis
Data encryption
Deep learning
Ideology
Intrusion detection systems
Machine learning
Methods
Network security
Students
Wavelet transforms
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Summary:In many colleges and universities, MOOCs have been applied in many courses, including ideological and political course, which is very important for college students’ ideological and moral education. Ideological and political MOOCs break the limitations of time and space, and students can conveniently and quickly learn ideological and political courses through the network. However, due to the openness of MOOCs, there may be some abnormal access behaviors, affecting the normal process of MOOCs. Therefore, in this paper, we propose a detection method of abnormal access behavior of ideological and political MOOCs in colleges and universities. Based on deep learning, the network behavior detection model is established to distinguish whether the network behavior is normal, so as to detect the abnormal access network behavior. In order to prove the effectiveness and efficiency of the proposed algorithm, the algorithm is compared with the other two network abnormal behavior detection methods, and the results prove that the proposed method can effectively detect the abnormal access behavior in ideological and political MOOCs.
ISSN:1574-017X
1875-905X
DOI:10.1155/2021/9977736