A Comprehensive Evaluation of Methods For DDoS Attack Classification

Distributed denial-of-service (DDoS) attacks may disrupt the normal functioning of the targeted networks, severely impacting internet services. Classifying distributed denial of service attacks is essential for developing effective mitigation measures, and this research aims to provide readers with...

Full description

Saved in:
Bibliographic Details
Published inInternational Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (Online) pp. 690 - 693
Main Authors Pipaliya, Bhakti, Gandhi, Ankita, Sutaria, Kruti
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.10.2024
Subjects
anomaly detection
Computer crime
cybersecurity
Deep learning
Denial-of-service attack
Distributed denial-of-service (DDoS) attacks
Grounding
machine learning
Machine learning algorithms
Organizations
Prevention and mitigation
Reviews
Solids
Web and internet services
Online AccessGet full text

Cover

More Information
Summary:Distributed denial-of-service (DDoS) attacks may disrupt the normal functioning of the targeted networks, severely impacting internet services. Classifying distributed denial of service attacks is essential for developing effective mitigation measures, and this research aims to provide readers with a solid grounding in the subject. Among this project's goals are the collection and organization of data pertaining to distributed denial of service attacks, as well as the investigation of potential defenses and countermeasures. The purpose of this article is to provide a comprehensive review of the present state of DDoS attack classification, with an emphasis on important deep learning and machine learning approaches. Beneficial to the field, the paper summarizes recent developments and suggests avenues for further study. In the case of effective DDoS detection, this is especially the case when choosing features and using deep learning algorithms.
ISSN:2768-0673
DOI:10.1109/I-SMAC61858.2024.10714886