Measuring the Histogram Feature Vector for Anomaly Network Traffic

Yan, Wei

doi:10.1007/11596981_41

Wei Yan²⁶

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3802))

Included in the following conference series:

International Conference on Computational and Information Science

902 Accesses

Abstract

Recent works have shown that Internet traffics are self- similar over several time scales from microseconds to minutes. On the other hand, the dramatic expansion of Internet applications give rise to a fundamental challenge to the network security. This paper presents a statistical analysis of the Internet traffic Histogram Feature Vector, which can be applied to detect the traffic anomalies. Besides, the Variant Packet Sending-interval Link Padding based on heavy-tail distribution is proposed to defend the traffic analysis attacks in the low or medium speed anonymity system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Chapter: USD 29.95; Price excludes VAT (USA)

eBook: USD 169.00; Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

References

Paxson, V., Floyd, S.: Wide-area traffic: the failure of Poisson modeling. In: Proceedings of ACM Sigcomm, pp. 257–268 (1994)
Google Scholar
Park, K., Willinger, W.: Self-similar network traffic and performance evaluation, pp. 17–19. John Wiley & Sons Inc., Chichester (2000)
Book Google Scholar
Mark, E., Bestavroe, A.: Explaining World Wide Web Traffic Self-Similarity. Technical Report TR-95-015 (1995)
Google Scholar
Sturges, H.: The choice of a class-interval. The American Statistical Association 21, 65–66 (1926)
Google Scholar
http://ita.ee.lbl.gov/html/traces.html
Doane, D.: Aesthetic frequency classification. American Statistician 30, 181–183 (1976)
Article Google Scholar

Download references

Author information

Authors and Affiliations

McAfee Inc,
Wei Yan

Authors

Wei Yan
View author publications
You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

Microelectronic Instiute, Xidian University, 710071, Xi’an, China
Yue Hao
Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Jiming Liu
School of Computer Science and Technology, Xidian University, Xi’an, China
Yu-Ping Wang
Department of Computer Science, Hong Kong Baptist University, Hong Kong,
Yiu-ming Cheung
School of Electrical and Electronic Engineering, University of Manchester, UK
Hujun Yin
Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China
Licheng Jiao
Key Laboratory of Computer Networks and Information Security(Ministry of Education), Xidian University, 710071, Xi’an, China
Jianfeng Ma
National Laboratory of Antennas and Microwave Technology, Xidian University, 710071, Xi’an, Shanxi, P.R. China
Yong-Chang Jiao

Rights and permissions

Reprints and permissions

Copyright information

About this paper

Cite this paper

Yan, W. (2005). Measuring the Histogram Feature Vector for Anomaly Network Traffic. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596981_41

Download citation

DOI: https://doi.org/10.1007/11596981_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30819-5
Online ISBN: 978-3-540-31598-8
eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics