A Novel Approach for DDoS Attack Detection Using Big Data and Machine Learning

Akshat Gaurav; Zhili Zhou; Kwok Tai Chui; Francesco COLACE; Priyanka Chaurasia; Ching-Hsien Hsu

A Novel Approach for DDoS Attack Detection Using Big Data and Machine Learning

Akshat Gaurav, Zhili Zhou, Kwok Tai Chui, Francesco COLACE, Priyanka Chaurasia, Ching-Hsien Hsu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Abstract

Due to the developemt in the latest digital technologies, internet service use has surged recently. In order for these online businesses to succeed, they must be able to consistently and effectively supply their services. As a result of the DDoS assault, online sources are impacted in terms of both their availability and their computational capacity. DDoS attacks are useful for cyber-attackers since there is no effective techniqque for the identification of them. In recent years, researchers have been experimenting with duffernet latest techniques like machine learning (ML) approaches to see whether they can build effective methods for detecting DDoS assaults.Machine learning and big data are used to identify DDoS
assaults in this research paper.

Original language	English
Title of host publication	Proceedings of International Conference on Smart Systems and Advanced Computing
Publisher	CEUR Workshop Proceedings
Pages	126-131
Number of pages	6
Volume	3080
Publication status	Published online - 27 Dec 2021
Event	International Conference on Smart Systems and Advanced Computing - Online Duration: 25 Dec 2021 → 26 Dec 2021 https://ceur-ws.org/

Publication series

Name	CEUR Workshop Proceedings
ISSN (Electronic)	1613-0073

Conference

Conference	International Conference on Smart Systems and Advanced Computing
Abbreviated title	Syscom-2021
Period	25/12/21 → 26/12/21
Internet address	https://ceur-ws.org/

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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A Novel Approach for DDoS Attack Detection...Final published version, 1.47 MBLicence: CC BY

https://ceur-ws.org/Vol-3080/18.pdfLicence: CC BY

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Gaurav, A., Zhou, Z., Chui, K. T., COLACE, F., Chaurasia, P., & Hsu, C.-H. (2021). A Novel Approach for DDoS Attack Detection Using Big Data and Machine Learning. In Proceedings of International Conference on Smart Systems and Advanced Computing (Vol. 3080, pp. 126-131). (CEUR Workshop Proceedings). CEUR Workshop Proceedings. Advance online publication. https://ceur-ws.org/Vol-3080/18.pdf

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title = "A Novel Approach for DDoS Attack Detection Using Big Data and Machine Learning",

abstract = "Due to the developemt in the latest digital technologies, internet service use has surged recently. In order for these online businesses to succeed, they must be able to consistently and effectively supply their services. As a result of the DDoS assault, online sources are impacted in terms of both their availability and their computational capacity. DDoS attacks are useful for cyber-attackers since there is no effective techniqque for the identification of them. In recent years, researchers have been experimenting with duffernet latest techniques like machine learning (ML) approaches to see whether they can build effective methods for detecting DDoS assaults.Machine learning and big data are used to identify DDoSassaults in this research paper.",

author = "Akshat Gaurav and Zhili Zhou and Chui, {Kwok Tai} and Francesco COLACE and Priyanka Chaurasia and Ching-Hsien Hsu",

year = "2021",

month = dec,

day = "27",

language = "English",

volume = "3080",

series = "CEUR Workshop Proceedings",

publisher = "CEUR Workshop Proceedings",

pages = "126--131",

booktitle = "Proceedings of International Conference on Smart Systems and Advanced Computing",

note = "International Conference on Smart Systems and Advanced Computing, Syscom-2021 ; Conference date: 25-12-2021 Through 26-12-2021",

url = "https://ceur-ws.org/",

}

Gaurav, A, Zhou, Z, Chui, KT, COLACE, F, Chaurasia, P & Hsu, C-H 2021, A Novel Approach for DDoS Attack Detection Using Big Data and Machine Learning. in Proceedings of International Conference on Smart Systems and Advanced Computing. vol. 3080, CEUR Workshop Proceedings, CEUR Workshop Proceedings, pp. 126-131, International Conference on Smart Systems and Advanced Computing, 25/12/21. <https://ceur-ws.org/Vol-3080/18.pdf>

A Novel Approach for DDoS Attack Detection Using Big Data and Machine Learning. / Gaurav, Akshat; Zhou, Zhili; Chui, Kwok Tai et al.
Proceedings of International Conference on Smart Systems and Advanced Computing. Vol. 3080 CEUR Workshop Proceedings, 2021. p. 126-131 (CEUR Workshop Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Novel Approach for DDoS Attack Detection Using Big Data and Machine Learning

AU - Gaurav, Akshat

AU - Zhou, Zhili

AU - Chui, Kwok Tai

AU - COLACE, Francesco

AU - Chaurasia, Priyanka

AU - Hsu, Ching-Hsien

PY - 2021/12/27

Y1 - 2021/12/27

N2 - Due to the developemt in the latest digital technologies, internet service use has surged recently. In order for these online businesses to succeed, they must be able to consistently and effectively supply their services. As a result of the DDoS assault, online sources are impacted in terms of both their availability and their computational capacity. DDoS attacks are useful for cyber-attackers since there is no effective techniqque for the identification of them. In recent years, researchers have been experimenting with duffernet latest techniques like machine learning (ML) approaches to see whether they can build effective methods for detecting DDoS assaults.Machine learning and big data are used to identify DDoSassaults in this research paper.

AB - Due to the developemt in the latest digital technologies, internet service use has surged recently. In order for these online businesses to succeed, they must be able to consistently and effectively supply their services. As a result of the DDoS assault, online sources are impacted in terms of both their availability and their computational capacity. DDoS attacks are useful for cyber-attackers since there is no effective techniqque for the identification of them. In recent years, researchers have been experimenting with duffernet latest techniques like machine learning (ML) approaches to see whether they can build effective methods for detecting DDoS assaults.Machine learning and big data are used to identify DDoSassaults in this research paper.

M3 - Conference contribution

VL - 3080

T3 - CEUR Workshop Proceedings

SP - 126

EP - 131

BT - Proceedings of International Conference on Smart Systems and Advanced Computing

PB - CEUR Workshop Proceedings

T2 - International Conference on Smart Systems and Advanced Computing

Y2 - 25 December 2021 through 26 December 2021

ER -

A Novel Approach for DDoS Attack Detection Using Big Data and Machine Learning

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