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  3. A Multi-class Intrusion Detection System For Ddos Attacks In Iot Networks Using Deep Learning And Transformers.
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  3. A Multi-class Intrusion Detection System For Ddos Attacks In Iot Networks Using Deep Learning And Transformers.

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A Multi-Class Intrusion Detection System for DDoS Attacks in IoT Networks Using Deep Learning and Transformers.

Sheikh Abdul Wahab1,2, Saira Sultana1, Noshina Tariq3

  • 1Department of Computing and Technology, H-9 Campus, Iqra University, Islamabad 44000, Pakistan.

Sensors (Basel, Switzerland)
|August 14, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a Deep Learning (DL) Intrusion Detection System (IDS) to combat Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks. The DL-based IDS effectively detects various DDoS threats, enhancing IoT security.

Keywords:
Convolutional Neural NetworkDeep LearningDistributed Denial of ServiceInternet of Things securityIntrusion Detection SystemSynthetic Minority Over-sampling TechniqueTransformeranomaly detection

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Area of Science:

  • Cybersecurity
  • Network Security
  • Artificial Intelligence

Background:

  • The proliferation of Internet of Things (IoT) devices has led to increased vulnerability to Distributed Denial of Service (DDoS) attacks.
  • DDoS attacks in IoT networks disrupt communication, compromise service availability, and cause significant operational and economic losses.

Purpose of the Study:

  • To develop and evaluate a Deep Learning (DL)-based Intrusion Detection System (IDS) specifically designed for IoT environments.
  • To assess the effectiveness of Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Transformer models in detecting various classes of DDoS attacks in IoT networks.

Main Methods:

  • Implementation of three DL architectures: CNNs, DNNs, and Transformer models for intrusion detection.
  • Utilized the CiC IoT 2023 dataset for training and testing the IDS models.
  • Employed data preprocessing techniques including log normalization and SMOTE-based oversampling to handle feature distributions and class imbalance.

    • Main Results:

    • Achieved high accuracy in binary classification (DNN: 99.2%, CNN: 99.0%, Transformer: 98.8%) for detecting DDoS attacks.
    • Demonstrated near-perfect performance (99.9-100%) in three-class classification (benign, DDoS, non-DDoS).
    • Reached strong accuracy in 12-class classification (DNN: 93.0%, CNN: 92.7%, Transformer: 92.5%) encompassing benign traffic and 12 attack types.

    Conclusions:

    • The proposed DL-based IDS demonstrates high efficacy, precision, recall, and ROC-AUC values for IoT DDoS detection.
    • The system outperforms existing state-of-the-art methods in detection accuracy and efficiency, offering a scalable solution.
    • Advanced DL models integrated into IDS frameworks provide a robust defense against evolving DDoS threats in IoT networks.