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 proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProceedings of International Conference on Smart Systems and Advanced Computing
PublisherCEUR Workshop Proceedings
Pages126-131
Number of pages6
Volume3080
Publication statusPublished online - 27 Dec 2021
EventInternational Conference on Smart Systems and Advanced Computing - Online
Duration: 25 Dec 202126 Dec 2021
https://ceur-ws.org/

Publication series

NameCEUR Workshop Proceedings
ISSN (Electronic)1613-0073

Conference

ConferenceInternational Conference on Smart Systems and Advanced Computing
Abbreviated titleSyscom-2021
Period25/12/2126/12/21
Internet address

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