Fast Tensor Factorization for Accurate Internet Anomaly Detection

doi:10.1109/TNET.2017.2761704

	Fast Tensor Factorization for Accurate Internet Anomaly Detection
	Xie, Kun 1,2; Li, Xiaocan 1; Wang, Xin 2; Xie, Gaogang 3; Wen, Jigang 3; Cao, Jiannong 4; Zhang, Dafang 1
	2017-12-01
发表期刊	IEEE-ACM TRANSACTIONS ON NETWORKING
ISSN	1063-6692
卷号	25 期号:6 页码:3794-3807
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.
关键词	Internet traffic anomaly detection tensor recovery tensor completion
DOI	10.1109/TNET.2017.2761704
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000418581900040
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：77[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/5524
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.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410012, Hunan, Peoples R China 2.SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA 3.Chinese Acad Sci, CAS Key Lab Network Data Sci & Technol, Inst Comp Technol, Beijing 100190, Peoples R China 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.