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Research on Detection Method of Interest Flooding Attack on Content Centric Network

Yabin Xu1, 2, 3, *, Ting Xu3, Xiaowei Xu4

1 Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing, 100101, China.
2 Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Information Science and Technology University, Beijing, 100101, China.
3 School of Computer, Beijing Information Science & Technology University, Beijing, 100101, China.
4 Department of Information Science, University of Arkansas at Little Rock, Little Rock, 72204, USA.

* Corresponding Author: Yabin Xu. Email: email.

Computers, Materials & Continua 2020, 64(2), 1075-1089. https://doi.org/10.32604/cmc.2020.09849

Abstract

To improve the attack detection capability of content centric network (CCN), we propose a detection method of interest flooding attack (IFA) making use of the feature of self-similarity of traffic and the information entropy of content name of interest packet. On the one hand, taking advantage of the characteristics of self-similarity is very sensitive to traffic changes, calculating the Hurst index of the traffic, to identify initial IFA attacks. On the other hand, according to the randomness of user requests, calculating the information entropy of content name of the interest packets, to detect the severity of the IFA attack, is. Finally, based on the above two aspects, we use the bilateral detection method based on non-parametric CUSUM algorithm to judge the possible attack behavior in CCN. The experimental results show that flooding attack detection method proposed for CCN can not only detect the attack behavior at the early stage of attack in CCN, but also is more accurate and effective than other methods.

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Cite This Article

Y. Xu, T. Xu and X. Xu, "Research on detection method of interest flooding attack on content centric network," Computers, Materials & Continua, vol. 64, no.2, pp. 1075–1089, 2020.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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