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Fast Tensor Factorization for Accurate Internet Anomaly Detection

文献类型：期刊论文

作者Xie, Kun2,3; Li, Xiaocan2; Wang, Xin3; Xie, Gaogang1; Wen, Jigang1; Cao, Jiannong4; Zhang, Dafang2
刊名IEEE-ACM TRANSACTIONS ON NETWORKING
出版日期2017-12-01
卷号25期号:6页码:3794-3807
关键词Internet traffic anomaly detection tensor recovery tensor completion
ISSN号1063-6692
DOI10.1109/TNET.2017.2761704
英文摘要Detecting anomalous traffic is a critical task for advanced Internet management. Many anomaly detection algorithms have been proposed recently. However, constrained by their matrix-based traffic data model, existing algorithms often suffer from low accuracy in anomaly detection. To fully utilize the multi-dimensional information hidden in the traffic data, this paper takes the initiative to investigate the potential and methodologies of performing tensor factorization for more accurate Internet anomaly detection. More specifically, we model the traffic data as a three-way tensor and formulate the anomaly detection problem as a robust tensor recovery problem with the constraints on the rank of the tensor and the cardinality of the anomaly set. These constraints, however, make the problem extremely hard to solve. Rather than resorting to the convex relaxation at the cost of low detection performance, we propose TensorDet to solve the problem directly and efficiently. To improve the anomaly detection accuracy and tensor factorization speed, TensorDet exploits the factorization structure with two novel techniques, sequential tensor truncation and two-phase anomaly detection. We have conducted extensive experiments using Internet traffic trace data Abilene and GEANT. Compared with the state of art algorithms for tensor recovery and matrix-based anomaly detection, TensorDet can achieve significantly lower false positive rate and higher true positive rate. Particularly, benefiting from our well designed algorithm to reduce the computation cost of tensor factorization, the tensor factorization process in TensorDet is 5 (Abilene) and 13 (GEANT) times faster than that of the traditional Tucker decomposition solution.
资助项目National Natural Science Foundation of China[61572184] ; National Natural Science Foundation of China[61472130] ; National Natural Science Foundation of China[61472131] ; National Natural Science Foundation of China[61725206] ; CAS Key Laboratory of Network Data Science and Technology (Institute of Computing Technology, Chinese Academy of Sciences)[CASNDST201704]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000418581900040
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/5523]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Xiaocan
作者单位1.Chinese Acad Sci, CAS Key Lab Network Data Sci & Technol, Inst Comp Technol, Beijing 100190, Peoples R China
2.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410012, Hunan, Peoples R China
3.SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA
4.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
推荐引用方式
GB/T 7714		 Xie, Kun,Li, Xiaocan,Wang, Xin,et al. Fast Tensor Factorization for Accurate Internet Anomaly Detection[J]. IEEE-ACM TRANSACTIONS ON NETWORKING,2017,25(6):3794-3807.
APA Xie, Kun.,Li, Xiaocan.,Wang, Xin.,Xie, Gaogang.,Wen, Jigang.,...&Zhang, Dafang.(2017).Fast Tensor Factorization for Accurate Internet Anomaly Detection.IEEE-ACM TRANSACTIONS ON NETWORKING,25(6),3794-3807.
MLA Xie, Kun,et al."Fast Tensor Factorization for Accurate Internet Anomaly Detection".IEEE-ACM TRANSACTIONS ON NETWORKING 25.6(2017):3794-3807.

入库方式： OAI收割

来源：计算技术研究所

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