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Analysis of DDoS Attacks Using Machine Learning Technique

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
T. Mallika Devi
A. Durga Bhavani
B. Chaitanya
B. Vijayalaxmi
Published in
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the critical role of machine learning in combating Distributed Denial of Service (DDoS) attacks, which can cripple online systems by overwhelming them with excessive traffic. The text explores the effectiveness of various machine learning algorithms, such as K-Nearest Neighbors (KNN) and Random Forest, in detecting and mitigating these attacks. It provides a detailed comparison of these algorithms, highlighting their advantages and limitations. The chapter also discusses the importance of data preparation, labeling, and model training in developing robust machine learning models for DDoS detection. Additionally, it presents experimental results that demonstrate the high accuracy of KNN and Random Forest algorithms in identifying DDoS attacks. The chapter concludes with a comprehensive framework for DDoS attack classification and prediction, offering a systematic approach to data utilization and efficient detection of these cyber threats.
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previous chapter Design and Simulation of a High-Speed 16-Bit Successive Approximation Register ADC (SAR ADC) with Capacitive-DAC Implementation Using Verilog
next chapter Privacy-Preserving Federated Machine Learning on Block Chain: A Comprehensive Review of Homomorphic Encryption Techniques
Title
Analysis of DDoS Attacks Using Machine Learning Technique
Authors
T. Mallika Devi
A. Durga Bhavani
B. Chaitanya
B. Vijayalaxmi
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering

Print ISBN: 978-981-9502-68-4

Electronic ISBN: 978-981-9502-69-1

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-0269-1_66
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