Rtvd: A real-time volumetric detection scheme for ddos in the internet of things

Li, J; Liu, M; Xue, Z; Fan, X; He, X

Rtvd: A real-time volumetric detection scheme for ddos in the internet of things

Li, J Liu, M Xue, Z Fan, X He, X

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Access, 2020, 8, pp. 36191-36201
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Li, J
dc.contributor.author	Liu, M
dc.contributor.author	Xue, Z
dc.contributor.author	Fan, X
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X
dc.date.accessioned	2020-11-03T23:12:30Z
dc.date.available	2020-11-03T23:12:30Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Access, 2020, 8, pp. 36191-36201
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/143740
dc.description.abstract	© 2013 IEEE. Distributed Denial of Service (DDoS) attacks are increasingly harmful to the cyberspace nowadays. The attackers can now easily launch a bigger and more challenging DDoS attack both towards and with Internet-of-Things (IoT) devices, due to the fast popularization of them. Because of the characteristic of fast overwhelming, it is important to make fast as well as accurate response to DDoS attacks, and the real-time performance can be even more important to prevent and legitimate the attacks. Among the methods proposed by researchers, the entropy-based detection method provides a sensitive and reliable performance. However, the balance between computational complexity and recognition accuracy remains a challenge. In this paper, we propose a detection method that consists of 3 main parts in different aspects: a sliding time window to fasten the entropy calculation, a single-directional filter to realize early detection during the DDoS progress but not after the crash, and a quintile deviation check algorithm to optimize the detection result. These will eventually lead to a real-time and high-efficient performance to recognize IoT DDoS attacks as soon as possible.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2020.2974293
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	Rtvd: A real-time volumetric detection scheme for ddos in the internet of things
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-11-03T23:12:19Z
pubs.publication-status	Published
pubs.volume	8

Abstract:

© 2013 IEEE. Distributed Denial of Service (DDoS) attacks are increasingly harmful to the cyberspace nowadays. The attackers can now easily launch a bigger and more challenging DDoS attack both towards and with Internet-of-Things (IoT) devices, due to the fast popularization of them. Because of the characteristic of fast overwhelming, it is important to make fast as well as accurate response to DDoS attacks, and the real-time performance can be even more important to prevent and legitimate the attacks. Among the methods proposed by researchers, the entropy-based detection method provides a sensitive and reliable performance. However, the balance between computational complexity and recognition accuracy remains a challenge. In this paper, we propose a detection method that consists of 3 main parts in different aspects: a sliding time window to fasten the entropy calculation, a single-directional filter to realize early detection during the DDoS progress but not after the crash, and a quintile deviation check algorithm to optimize the detection result. These will eventually lead to a real-time and high-efficient performance to recognize IoT DDoS attacks as soon as possible.

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http://hdl.handle.net/10453/143740