@Article{cmc.2020.09849,
AUTHOR = {Yabin Xu, Ting Xu, Xiaowei Xu},
TITLE = {Research on Detection Method of Interest Flooding Attack on Content Centric Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {1075--1089},
URL = {http://www.techscience.com/cmc/v64n2/39347},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.09849}
}