CCID: Cross-Correlation Identity Distinction Method for Detecting Shrew DDoS

dc.contributorTardif, Pierre-Martin
dc.contributor.authorHuang, Cheng
dc.contributor.authorYi, Ping
dc.contributor.authorZou, Futai
dc.contributor.authorYao, Yao
dc.contributor.authorWang, Wei
dc.contributor.authorZhu, Ting
dc.date.accessioned2019-03-13T14:47:58Z
dc.date.available2019-03-13T14:47:58Z
dc.date.issued2019-02-20
dc.description.abstractThis study presents a new method for detecting ShrewDDoS (DistributedDenial of Service) attacks and analyzes the characteristics of the Shrew DDoS attack. Shrew DDoS is periodic to be suitable for the server’s TCP (Transmission Control Protocol) timer. It has lower maximum to bypass peak detection.This periodicity makes it distinguishable from normal data packets. By proposing the CCID (Cross-Correlation Identity Distinction) method to distinguish the flow properties, it quantifies the difference between a normal flow and an attack flow. Simultaneously, we calculated the cross-correlation between the attack flow and the normal flow in three different situations.The server can use its own TCP flow timer to construct a periodic attack flow.The cross-correlation between Gaussian white noise and simulated attack flow is less than 0.3.The cross-correlation between single-door function and simulated attack flow is 0.28. The cross-correlation between actual attack flow and simulated attack flow is more than 0.8. This shows that we can quantitatively distinguish the attack effects of different signals. By testing 4 million data, we can prove that it has a certain effect in practice.en_US
dc.description.sponsorshipThis work is supported by the National Natural Science Foundation of China (61571290, 61831007, and 61431008), National Key Research and Development Program of China (2017YFB0802900, 2017YFB0802300, and 2018YFB0803503), the NSFC Zhejiang Joint Fund for the Integration of Industrialization and Informationization under grant (U1509219), Shanghai Municipal Science and Technology Project under grants (16511102605, 16DZ1200702), Information Network Security Key Laboratory of the Ministry of Public Security Open Project Support (C18611), and NSF grants 1652669 and 1539047.en_US
dc.description.urihttps://www.hindawi.com/journals/wcmc/2019/6705347/en_US
dc.format.extent10 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m23prq-p7kq
dc.identifier.citationCheng Huang, Ping Yi, Futai Zou, Yao Yao, Wei Wang, and Ting Zhu, CCID: Cross-Correlation Identity Distinction Method for Detecting Shrew DDoS, Wireless Communications and Mobile Computing Volume 2019, Article ID 6705347, 9 pages, https://doi.org/10.1155/2019/6705347en_US
dc.identifier.urihttps://doi.org/10.1155/2019/6705347
dc.identifier.urihttp://hdl.handle.net/11603/13027
dc.language.isoen_USen_US
dc.publisherHindawien_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution 4.0 International (CC BY 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectDDoS (Distributed Denial of Service)en_US
dc.subjectTCP (Transmission Control Protocol)en_US
dc.subjectCCID (Cross-Correlation Identity Distinction) methoden_US
dc.titleCCID: Cross-Correlation Identity Distinction Method for Detecting Shrew DDoSen_US
dc.typeTexten_US

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