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Securing IoT Networks Against DDoS Attacks: A Hybrid Deep Learning Approach.

Noor Ul Ain1, Muhammad Sardaraz1, Muhammad Tahir1

  • 1Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
Summary

This study introduces a hybrid deep learning model for detecting Distributed Denial-of-Service (DDoS) attacks in Internet of Things (IoT) networks. The novel approach significantly improves detection accuracy, outperforming existing methods on the CICIOT2023 dataset.

Keywords:
DDoSInternet of Thingsclassification accuracyconvolutional neural networksdeep learning

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Area of Science:

  • Cybersecurity
  • Network Security
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) is rapidly expanding, leading to increased network complexity and vulnerability.
  • Growing interconnectivity in IoT networks presents significant security challenges, particularly concerning Distributed Denial-of-Service (DDoS) attacks.
  • Effective detection of sophisticated DDoS attacks is crucial for maintaining the integrity and availability of IoT systems.

Purpose of the Study:

  • To apply and evaluate deep learning techniques for enhanced DDoS attack detection in IoT environments.
  • To propose and validate a novel hybrid deep learning model for improved IoT network security against DDoS threats.
  • To assess the performance of various deep learning architectures in identifying complex attack patterns.

Main Methods:

  • Assessed performance of Latent Autoencoders, LSTM Autoencoders, and Convolutional Neural Networks (CNNs) for DDoS detection.
  • Developed a hybrid model integrating CNNs (feature extraction), Long Short-Term Memory (LSTM) networks (temporal patterns), and Autoencoders (dimensionality reduction).
  • Utilized the CICIOT2023 dataset for experimental evaluation and performance comparison.

Main Results:

  • The proposed hybrid model achieved high accuracy, with 96.78% training accuracy and 96.60% validation accuracy.
  • Experimental results demonstrated that the hybrid model significantly outperformed individual deep learning models in detecting DDoS attacks.
  • The model showed efficiency in addressing complex attack patterns prevalent in modern IoT networks.

Conclusions:

  • The developed hybrid deep learning model offers a robust solution for DDoS attack detection in IoT networks.
  • Further research is needed to address limitations in detecting rare attack types and data imbalance issues.
  • Enhancing DDoS detection capabilities requires continued focus on advanced deep learning techniques and comprehensive datasets.