Holistic View on Detecting DDoS Attacks Using Machine Learning

Eduardo Barros, Victor Lobo, Anacleto Correia

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Distributed denial of service (DDoS) attacks are an enormous threat, mainly because of the extension they can reach, the ease of deployment, the losses that it can cause, and the effort it can take to detect and stop this type of attack. Machine learning techniques have been and are widely used to prevent DDoS attacks. As a matter of fact, many gigantic intrusion detection systems (IDS) have been proudly utilising machine learning techniques to help the conventional signature detection system by adding another layer of “intelligent” thinking. This chapter provides a context of the techniques used for detecting DDoS attacks using machine learning, and in demonstrating why the merge of these concepts have huge potential for the defence of a given system. To that matter, some studies that use machine learning approaches for DDoS detection are analysed. Finally, this chapter provides a high-level view of the types of DDoS attacks that are considered a threat, the machine learning approaches to detect these attacks, and why these approaches are cohesive.
Original languageEnglish
Title of host publicationApplications of Machine Learning and Deep Learning for Privacy and Cybersecurity
EditorsVictor Lobo, Anacleto Correia
PublisherIGI Global
Number of pages17
ISBN (Electronic)9781799894322
ISBN (Print)9781799894308
Publication statusPublished - 24 Jun 2022


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