Promising Techniques for Anomaly Detection on Network Traffic

被引：0

作者：

Tian, Hui ^{[1
,2
]}

Liu, Jingtian ^{[1
]}

Ding, Meimei ^{[1
]}

机构：

[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing, Peoples R China

[2] Univ Adelaide, Sch Comp Sci, Adelaide, SA, Australia

来源：

COMPUTER SCIENCE AND INFORMATION SYSTEMS | 2017年 / 14卷 / 03期

基金：

澳大利亚研究理事会;

关键词：

diffusion wavelet; principal component analysis; anomaly detection; WAVELET-BASED ANALYSIS;

D O I：

10.2298/CSIS170201018H

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：597 / 609

页数：13

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