Computer Science ›› 2022, Vol. 49 ›› Issue (7): 170-178.doi: 10.11896/jsjkx.210600092

• Artificial Intelligence • Previous Articles     Next Articles

Method for Abnormal Users Detection Oriented to E-commerce Network

DU Hang-yuan1, LI Duo1, WANG Wen-jian1,2   

  1. 1 School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
    2 Key Laboratory of Computational Intelligence and Chinese Information Processing(Shanxi University),Ministry of Education,Taiyuan 030006,China
  • Received:2021-06-10 Revised:2021-10-25 Online:2022-07-15 Published:2022-07-12
  • About author:DU Hang-yuan,born in 1985,Ph.D,associate professor,master supervisor.His main research interests include cluster analysis and complex network theory.
    WANG Wen-jian,born in 1968,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.Her main research interests include machine learning,data mining and computational intelligence.
  • Supported by:
    National Natural Science Foundation of China(61902227,62076154,U1805263),Special Foundation from the Central Finance to Support the Development of Local University(YDZX20201400001224),Natural Science Foundation of Shanxi Province,China(201901D211192) and Science Foundation of the Higher Education Institutions of Shanxi Province,China(2019L0039).

Abstract: In the e-commerce network,abnormal users often show different behavioral characteristics from normal users.Detecting abnormal users and analyzing their behavior patterns is of great practical significance to maintaining the order of e-commerce platforms.By analyzing the behavior patterns of abnormal users,we abstract the e-commerce network into the heterogeneous information network,and convert it into a user-device bipartite graph.On this basis,we propose a method for detecting abnormal users oriented to e-commerce network——self-supervised anomaly detection model(S-SADM).The model has a self-supervised learning mechanism.It uses an autoencoder to encode the user-device bipartite graph to obtain user node representations.By optimizing the joint objective function,the model completes backpropagation,and uses support vector data descriptions to perform anomaly detection on user node representations.After the automatic iterative optimization of the network,the user node representation has supervised information,and we obtain relatively stable detection results.Finally,S-SADM is validated on 3 real network datasets and a semi-synthetic network dataset,and the experimental results demonstrate the effectiveness and superiority of the method.

Key words: Anomaly detection, Autoenco-der, E-commerce network, Heterogeneous information network, Self-supervised learning, Support vector data description

CLC Number: 

  • TP183
[1]HU C P,QIN X L.A density-based local outlier detection algorithm[J].Journal of Computer Research and Development,2010,47(12):2110-2116.
[2]REN J D,LIU X Q,WANG Q,et al.Multi-layer intrusion detection method based on KNN outlier detection and random forest[J].Journal of Computer Research and Development,2019,56(3):116-125.
[3]RASHEED F,ALHAJJ R.A framework for periodic outlier pattern detection in time-series sequences[J].IEEE Transactions on Cybernetics,2013,44(5):569-582.
[4]CHAKRABORTY D,NARAYANAN V,GHOSH A.Integration of deep feature extraction and ensemble learning for outlier detection[J].Pattern Recognition,2019,89:161-171.
[5]WU S,WANG S R.Information-theoretic outlier detection for large-scale categorical data[J].IEEE Transactions on Know-ledge and Data Engineering,2013,25(3):589-602.
[6]LIU L,ZUO W L,PENG T.Dynamic outlier detection method based on tensor representation in heterogeneous network[J].Journal of Computer Research and Development,2016,53(8):1729-1739.
[7]LIU Z Q,CHEN C C,YANG X X,et al.Heterogeneous graph neural networks for malicious account detection[C]//ACM International Conference.New York:ACM,2018:2077-2085.
[8]GUPTA M,GAO J,HAN J W.Community distribution outlier detection in heterogeneous information networks[C]//Joint European Conference on Machine Learning & Knowledge Disco-very in Databases.Berlin:Springer,2013:11-29.
[9]WANG H B,ZHOU C,WU J,et al.Deep structure learning for fraud detection[C]//IEEE International Conference on Data Mining.NJ:IEEE,2018:567-576.
[10]ZHENG M Y,ZHOU C,WU J,et al.FraudNE:a Joint Embedding Approach for Fraud Detection International[C]//Joint Conference on Neural Networks.NJ:IEEE,2018:4739-4746.
[11]DONG M Q,YAO L N,WANG X Z,et al.Opinion fraud detection via neural autoencoder decision forest[J].Pattern Recognition Letters,2020,132:21-29.
[12]RUMELHART D E,HINTON G E,WILLIAMS R J.Learning representations by back-propagating errors[J].Nature,1986,323(6088):533-536.
[13]BOURLARD H,KAMP Y.Auto-assoation by multilayer perceptrons and singular value decomposition[J].Biological Cybernetics,1988,59(4):291-294.
[14]YUAN F N,ZHANG L,SHI J T,et al.Overview of auto-encoding neural network theory and application[J].Chinese Journal of Computers,2019,42(1):203-230.
[15]LI E Z,DU P J,SAMAT A,et al.Mid-level feature representation via sparse autoencoder for remotely sensed scene classification[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017,10(3):1068-1081.
[16]GENG J,WANG H Y,FAN J C,et al.Deep supervised and contractive neural network for sar image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2017,4:1-18.
[17]HASAN M,CHOI J.NEUMANN J,et al.Learning temporalregularity in video sequences[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.NJ:IEEE.2016:733-742.
[18]RIBEIRO M,LAZZARETTI A E,LOPES H S.A study of deep convolutional auto-encoders for anomaly detection in videos[J].Pattern Recognition Letters,2018,105:13-22.
[19]GAO S H,ZHANG Y T,JIA K,et al.Single sample face recognition via learning deep supervised autoencoders[J].IEEE Transactions on Information Forensics and Security,2015,10(10):2108-2118.
[20]RUFF L,VANDERMEULEN R,GOERNITZ N,et al.Deepone-class classification[C]//International conference on machine learning.Cambridge MA:JMLR,2018:4390-4399.
[21]LIU T,TING K M,ZHOU Z H.Isolation-based anomaly detection[J].ACM Transactions on Knowledge Discovery from Data,2012,6(1):1-39.
[22]NAGANJANEYULU S,KUPPA M R.A novel framework for class imbalance learning using intelligent under-sampling[J].Progress in Artificial Intelligence,2013,2(1):73-84.
[23]JIANG K,LU J,XIA K L.A novel algorithm for imbalance data classification based on genetic algorithm improved smote[J].Arabian Journal for Science & Engineering,2016,41(8):3255-3266.
[24]ZHOU Z H.Machine Learning[M].Beijing:Tsinghua Univer-sity Press,2016:42-43.
[1] LYU Xiao-feng, ZHAO Shu-liang, GAO Heng-da, WU Yong-liang, ZHANG Bao-qi. Short Texts Feautre Enrichment Method Based on Heterogeneous Information Network [J]. Computer Science, 2022, 49(9): 92-100.
[2] XU Tian-hui, GUO Qiang, ZHANG Cai-ming. Time Series Data Anomaly Detection Based on Total Variation Ratio Separation Distance [J]. Computer Science, 2022, 49(9): 101-110.
[3] WANG Xin-tong, WANG Xuan, SUN Zhi-xin. Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network [J]. Computer Science, 2022, 49(8): 314-322.
[4] SHEN Shao-peng, MA Hong-jiang, ZHANG Zhi-heng, ZHOU Xiang-bing, ZHU Chun-man, WEN Zuo-cheng. Three-way Drift Detection for State Transition Pattern on Multivariate Time Series [J]. Computer Science, 2022, 49(4): 144-151.
[5] WU Yu-kun, LI Wei, NI Min-ya, XU Zhi-cheng. Anomaly Detection Model Based on One-class Support Vector Machine Fused Deep Auto-encoder [J]. Computer Science, 2022, 49(3): 144-151.
[6] LENG Jia-xu, TAN Ming-pi, HU Bo, GAO Xin-bo. Video Anomaly Detection Based on Implicit View Transformation [J]. Computer Science, 2022, 49(2): 142-148.
[7] JIANG Zong-li, FAN Ke, ZHANG Jin-li. Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(1): 133-139.
[8] ZHENG Su-su, GUAN Dong-hai, YUAN Wei-wei. Heterogeneous Information Network Embedding with Incomplete Multi-view Fusion [J]. Computer Science, 2021, 48(9): 68-76.
[9] ZHANG Ye, LI Zhi-hua, WANG Chang-jie. Kernel Density Estimation-based Lightweight IoT Anomaly Traffic Detection Method [J]. Computer Science, 2021, 48(9): 337-344.
[10] ZHAO Jin-long, ZHAO Zhong-ying. Recommendation Algorithm Based on Heterogeneous Information Network Embedding and Attention Neural Network [J]. Computer Science, 2021, 48(8): 72-79.
[11] TIAN Song-wang, LIN Su-zhen, YANG Bo. Multi-band Image Self-supervised Fusion Method Based on Multi-discriminator [J]. Computer Science, 2021, 48(8): 185-190.
[12] QING Lai-yun, ZHANG Jian-gong, MIAO Jun. Temporal Modeling for Online Anomaly Detection [J]. Computer Science, 2021, 48(7): 206-212.
[13] GUO Yi-shan, LIU Man-dan. Anomaly Detection Based on Spatial-temporal Trajectory Data [J]. Computer Science, 2021, 48(6A): 213-219.
[14] XING Hong-jie, HAO ZhongHebei. Novelty Detection Method Based on Global and Local Discriminative Adversarial Autoencoder [J]. Computer Science, 2021, 48(6): 202-209.
[15] ZOU Cheng-ming, CHEN De. Unsupervised Anomaly Detection Method for High-dimensional Big Data Analysis [J]. Computer Science, 2021, 48(2): 121-127.
Full text



No Suggested Reading articles found!