DocumentCode
109160
Title
Network traffic signature generation mechanism using principal component analysis
Author
Mu Cheng ; Huang Xiaohong ; Wu Jun ; Ma Yan
Author_Institution
Inst. of Network Technol., Beijing Univ. of Posts & Telecommun., Beijing, China
Volume
10
Issue
11
fYear
2013
fDate
Nov. 2013
Firstpage
95
Lastpage
106
Abstract
The Deep Packet Inspection (DPI) method is a popular method that can accurately identify the flow data and its corresponding application. Currently, the DPI method is widely used in common network management systems. However, the major limitation of DPI systems is that their signature library is mainly extracted manually, which makes it hard to efficiently obtain the signature of new applications. Hence, in this paper, we propose an automatic signature extraction mechanism using Principal Component Analysis (PCA) technology, which is able to extract the signature automatically. In the proposed method, the signatures are expressed in the form of serial consistent sequences constructed by principal components instead of normally separated substrings in the original data extracted from the traditional methods. Extensive experiments based on numerous sets of data have been carried out to evaluate the performance of the proposed scheme, and the results prove that the newly proposed method can achieve good performance in terms of accuracy and efficiency.
Keywords
Internet; computer network management; computer network security; digital signatures; principal component analysis; telecommunication traffic; DPI systems; Internet service providers; PCA; automatic signature extraction mechanism; common network management systems; deep packet inspection method; network traffic signature generation mechanism; principal component analysis technology; serial consistent sequences; signature library; Algorithm design and analysis; Data mining; Ports (Computers); Principal component analysis; Telecommunication traffic; automatic signature generation; deep packet inspection; network traffic classification; principal component analysis;
fLanguage
English
Journal_Title
Communications, China
Publisher
ieee
ISSN
1673-5447
Type
jour
DOI
10.1109/CC.2013.6674214
Filename
6674214
Link To Document