CAE-IF:An Anomaly Detection Approach Based on Temporal Representation of the Reconstruction ErrorOA
CAE-IF:An Anomaly Detection Approach Based on Temporal Representation of the Reconstruction Error
Haijun Geng;Zhi Zhang;Yuhua Qian;Bo Yang;Qi Ma;Haotian Chi;Jing Yang;Xia Yin
School of Automation and Software Engineering,Shanxi University,Taiyuan 030006,China||Shanxi Qingzhong Technology Co.Ltd.,Taiyuan 030032,ChinaSchool of Automation and Software Engineering,Shanxi University,Taiyuan 030006,ChinaInstitute of Big Data Science and Industry,Shanxi University,Taiyuan 030006,ChinaDepartment of Urban & Regional Planning San José State University,San José,CA 95192,USASchool of Automation and Software Engineering,Shanxi University,Taiyuan 030006,ChinaSchool of Automation and Software Engineering,Shanxi University,Taiyuan 030006,ChinaSchool of Automation and Software Engineering,Shanxi University,Taiyuan 030006,ChinaDepartment of Computer Science and Technology,Tsinghua University,Beijing 100084,China
unsupervised learningnetwork traffic detectionreconstruction errortemporal representation
unsupervised learningnetwork traffic detectionreconstruction errortemporal representation
《清华大学学报自然科学版（英文版）》 2026 (4)
2055-2070,16
This work was in part supported by the National Natural Science Foundation of China(Nos.62472267,62302282,and 62406181),the Shanxi Province Science Foundation(Nos.202203021222005 and 202203021222010),the Postgraduate Research Innovation Program of Shanxi Province(No.2024KY137),and the Scientific and Technological Innovation 2030—"New Generation Artificial Intelligence"Major Project(No.2021ZD0112400).
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