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Wireless Personal Communications

Towards Detection of DDoS Attacks in IoT with Optimal Features Selection

  • 15-07-2024
Published in:
Authors
Pooja Kumari
Ankit Kumar Jain
Yash Pal
Kuldeep Singh
Anubhav Singh
Published in
Wireless Personal Communications | Issue 2/2024

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Abstract

The proliferation of IoT devices has led to an increase in DDoS attacks, posing significant security threats. This article explores the use of machine learning and deep learning algorithms to detect DDoS attacks in IoT networks, focusing on the critical role of feature selection in enhancing detection accuracy. By comparing various classification models and evaluating their performance, the study offers valuable insights into the most effective approaches for safeguarding IoT systems against DDoS attacks. The research highlights the need for advanced detection mechanisms to combat the evolving tactics of cyber attackers, making it a must-read for cybersecurity professionals and researchers.
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Title
Towards Detection of DDoS Attacks in IoT with Optimal Features Selection
Authors
Pooja Kumari
Ankit Kumar Jain
Yash Pal
Kuldeep Singh
Anubhav Singh
Publication date
15-07-2024
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 2/2024
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-024-11419-2
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