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

Year : 2026 | Volume : 13 | Issue : 01 | Page : 23 33
    By

    Manjusha V. Khond,

  • Mahesh R. Sanghavi,

  1. Research Scholar, Computer Engineering, Mumbai Educational Trust, Institute of Engineering, Bhujbal Knowledge City, Nashik, Maharashtra, India
  2. Professor & Vice-Principal, Computer Engineering, 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 rapid expansion. The intricacy and dynamic character of these advanced attacks can provide a challenge to conventional intrusion detection systems. This study presents a novel method for strengthening IoT network security by combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks into a hybrid deep learning framework. 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 study 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

[This article belongs to Journal of Web Engineering & Technology ]

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):23-33.
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):23-33. Available from: https://journals.stmjournals.com/jowet/article=2025/view=232419


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