Computer and Modernization ›› 2022, Vol. 0 ›› Issue (12): 13-17.

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Anomaly Detection of Student Consumption Data Based on Semi-supervised Learning


  1. (1. Information Construction Department, China University of Petroleum (East China), Qingdao 266580, China; 
    2. College of Computer and Communication Engineering, China University of Petroleum (East China), Qingdao 266580, China)
  • Online:2023-01-04 Published:2023-01-04

Abstract: With the more and more extensive application scenarios of campus card, the problem of capital security of campus card has become increasingly prominent. Campus card fraud will not only bring economic losses to teachers, students and businesses in the school, but also endanger the normal order of the campus. Aiming at the problem that the traditional anomaly detection method can not effectively extract the temporal feature of student consumption data, this paper proposes an anomaly detection method of student consumption data based on semi-supervised learning. Firstly, the auto-encoder is enhanced with the Gated Recurrent Unit, so that the model can reconstruct the consumption data more accurately. Then, the reconstruction error is calculated by Mahalanobis Distance, and the error threshold is determined by Fβ-Socre to detect abnormal data. Finally, the proposed method is used to detect the anomaly of student consumption data in a university. Experimental results show that the proposed method has better detection performance.

Key words: deep learning, data mining, auto-encoder, anomaly detection