文章基本信息

标题：Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities
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作者：Ni Hong ; Xuefeng Wang ; Zhonghua Wang 等
期刊名称：Mobile Information Systems
印刷版ISSN：1574-017X
出版年度：2021
卷号：2021
页码：1-9
DOI：10.1155/2021/9977736
出版社：Hindawi Publishing Corporation
摘要：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.