DDoS Detection Using Cascade Correlation for Improving Network Resources in Cloud Environment

Year : 2025 | Volume : 03 | Issue : 02 | Page : 17 22
    By

    D. Prabakar,

  • Naveena M.,

  • Deepika S.,

  • Pavithra K.,

  1. Professor & Head of the Department, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  2. UG Scholar, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  3. UG Scholar, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  4. UG Scholar, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India

Abstract

Intrusion detection is critical for protecting network security from emerging cyber threats. This study describes a unique intrusion detection system (IDS) based on the Random Forest algorithm. Random Forests are used as an effective classifier to identify patterns linked with malevolent behaviour. This technique uses Random Forests to improve the accuracy and efficiency of intrusion detection systems. The suggested methodology’s value is shown by its performance on the benchmark KDD Cup dataset, which shows significant gains in detection accuracy when compared to previous methodologies. This study improves intrusion detection technology by demonstrating Random Forests’ effectiveness in overcoming the hurdles presented by complex assaults. The system employs Random Forests as its primary classification method to accurately detect and analyze behavioral patterns linked to different types of cyberattacks, including denial-of-service (DoS), probing, remote-to-local (R2L), and user-to-root (U2R) intrusions. By generating numerous decision trees during the training phase and determining the final output based on the majority vote among these trees, Random Forests deliver improved performance, especially in managing noisy datasets and minimizing the risk of overfitting—issues that often challenge conventional intrusion detection methods.

Keywords: DDoS, intrusion detection, machine leaning, random forests, cloud environment

[This article belongs to International Journal of Wireless Security and Networks ]

How to cite this article:
D. Prabakar, Naveena M., Deepika S., Pavithra K.. DDoS Detection Using Cascade Correlation for Improving Network Resources in Cloud Environment. International Journal of Wireless Security and Networks. 2025; 03(02):17-22.
How to cite this URL:
D. Prabakar, Naveena M., Deepika S., Pavithra K.. DDoS Detection Using Cascade Correlation for Improving Network Resources in Cloud Environment. International Journal of Wireless Security and Networks. 2025; 03(02):17-22. Available from: https://journals.stmjournals.com/ijwsn/article=2025/view=232822


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Regular Issue Subscription Review Article
Volume 03
Issue 02
Received 24/03/2025
Accepted 11/07/2025
Published 09/09/2025
Publication Time 169 Days
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