Enhancing IoT Network Security with Hybrid Deep Learning Classifiers for DDoS Attack Detection

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Year : 2025 | Volume : 13 | 01 | Page :
    By

    Manjusha V. Khond,

  • Mahesh R. Sanghavi,

  1. Research Scholar, MET IOE BKC Nashik, Maharashtra, India
  2. Professor, S.N.J.B. Engineering College Chandwad, Maharashtra, India

Abstract

The security and operational dependability of Internet of Things (IoT) networks are seriously threatened by the growing susceptibility to Distributed Denial of Service (DDoS) assaults brought about by their fast expansion. The intricacy and dynamic character of these advanced attacks can provide a challenge to conventional intrusion detection systems. This paper introduces a new approach to enhancing IoT network security through the integration of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks within a hybrid deep learning model. The suggested CNN-LSTM approach ensures precise and effective DDoS threat detection by utilizing CNNs’ advantages for feature extraction and LSTMs for temporal dependency modelling. The capacity of the model to minimize security threats is shown by the assessment findings, which indicate that it performs better than existing methods in accuracy, precision, and recall. This paper highlights the strength of hybrid deep learning models as a solid solution for safeguarding IoT ecosystems against sophisticated cyber-attacks.

Keywords: Hybrid Deep Learning classifiers, CNN, LSTM, DDoS, IoT.

How to cite this article:
Manjusha V. Khond, Mahesh R. Sanghavi. Enhancing IoT Network Security with Hybrid Deep Learning Classifiers for DDoS Attack Detection. Journal of Web Engineering & Technology. 2025; 13(01):-.
How to cite this URL:
Manjusha V. Khond, Mahesh R. Sanghavi. Enhancing IoT Network Security with Hybrid Deep Learning Classifiers for DDoS Attack Detection. Journal of Web Engineering & Technology. 2025; 13(01):-. Available from: https://journals.stmjournals.com/jowet/article=2025/view=232419


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Ahead of Print Subscription Review Article
Volume 13
01
Received 12/06/2025
Accepted 05/09/2025
Published 17/11/2025
Publication Time 158 Days
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