Geodesic
Promising Techniques for Anomaly Detection on Network Traffic
Computer Science and Information Systems, Tome 14 (2017) no. 3

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In various networks, anomaly may happen due to network breakdown, intrusion detection, and end-to-end traffic changes. To detect these anomalies is important in diagnosis, fault report, capacity plan and so on. However, it’s challenging to detect these anomalies with high accuracy rate and time efficiency. Existing works are mainly classified into two streams, anomaly detection on link traffic and on global traffic. In this paper we discuss various anomaly detection methods on both types of traffic and compare their performance.
Keywords: diffusion wavelet, principal component analysis, anomaly detection 
Hui Tian; Jingtian Liu; Meimei Ding. Promising Techniques for Anomaly Detection on Network Traffic. Computer Science and Information Systems, Tome 14 (2017) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2017_14_3_a4/
@article{CSIS_2017_14_3_a4,
     author = {Hui Tian and Jingtian Liu and Meimei Ding},
     title = {Promising {Techniques} for {Anomaly} {Detection} on {Network} {Traffic}},
     journal = {Computer Science and Information Systems},
     year = {2017},
     volume = {14},
     number = {3},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2017_14_3_a4/}
}
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