Utilizing Machine Learning and Deep Learning Techniques for the Detection of Distributed Denial of Service (DDoS) Attacks

Salim Badar Salim Hamed Al-Hajri, Paul Jenkins*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The growing number of cyber-attacks has heightened the need for robust security measures, as they escalate in frequency and impact, affecting both economic stability and personal safety. Traditional methods of detecting these cyber threats are often costly and slow, prompting the need for more efficient and accurate technologies. This study explores advanced artificial intelligence techniques, utilizing both Machine Learning (ML) and Deep Learning (DL), to enhance the detection of Distributed Denial of Service (DDoS) attacks, by integrating diverse AI methodologies, including deep neural networks, random forest, long short-term memory and extreme gradient boosting systems, moreover, the paper investigates their collective effectiveness on the CICIDS2017 dataset. The analysis confirms that these integrated AI approaches achieve significant accuracy, recall and low false positive rates in identifying DDoS incidents. The paper is constructed as follows, Section 1 – Introduction, Section 2 A review of AI methods, Section 3 - Evolution of proposed models, Section 4 Experimental results, and Sect. 5 Discusses possible areas for further research.

Original languageEnglish
Title of host publicationContributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI - The C3AI 2024
EditorsNitin Naik, Paul Grace, Paul Jenkins, Shaligram Prajapat
PublisherSpringer Science and Business Media Deutschland GmbH
Pages223-235
Number of pages13
ISBN (Print)9783031744426
DOIs
Publication statusPublished - 20 Dec 2024
EventInternational Conference on Computing, Communication, Cybersecurity and AI, C3AI 2024 - London, United Kingdom
Duration: 3 Jul 20244 Jul 2024

Publication series

NameLecture Notes in Networks and Systems
Volume884 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceInternational Conference on Computing, Communication, Cybersecurity and AI, C3AI 2024
Country/TerritoryUnited Kingdom
CityLondon
Period3/07/244/07/24

Keywords

  • AI Artificial Intelligence
  • Cybersecurity
  • DDoS – Distributed Denial of Service
  • DL – Deep Learning
  • ML – Machine Learning

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