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Article

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

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Department of Computing and Technology, H-9 Campus, Iqra University, Islamabad 44000, Pakistan
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Department of Avionics Engineering, Main Campus PAF Complex E-9, Air University, Islamabad 44000, Pakistan
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Department of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan
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Department of Computer Science, Cybersecurity and Computing Systems Research Group, University of Hertfordshire, Hertfordshire AL10 9AB, UK
*
Authors to whom correspondence should be addressed.
Sensors 2025, 25(15), 4845; https://doi.org/10.3390/s25154845
Submission received: 11 June 2025 / Revised: 21 July 2025 / Accepted: 4 August 2025 / Published: 6 August 2025
(This article belongs to the Section Internet of Things)

Abstract

The rapid proliferation of Internet of Things (IoT) devices has significantly increased vulnerability to Distributed Denial of Service (DDoS) attacks, which can severely disrupt network operations. DDoS attacks in IoT networks disrupt communication and compromise service availability, causing severe operational and economic losses. In this paper, we present a Deep Learning (DL)-based Intrusion Detection System (IDS) tailored for IoT environments. Our system employs three architectures—Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Transformer-based models—to perform binary, three-class, and 12-class classification tasks on the CiC IoT 2023 dataset. Data preprocessing includes log normalization to stabilize feature distributions and SMOTE-based oversampling to mitigate class imbalance. Experiments on the CIC-IoT 2023 dataset show that, in the binary classification task, the DNN achieved 99.2% accuracy, the CNN 99.0%, and the Transformer 98.8%. In three-class classification (benign, DDoS, and non-DDoS), all models attained near-perfect performance (approximately 99.9–100%). In the 12-class scenario (benign plus 12 attack types), the DNN, CNN, and Transformer reached 93.0%, 92.7%, and 92.5% accuracy, respectively. The high precision, recall, and ROC-AUC values corroborate the efficacy and generalizability of our approach for IoT DDoS detection. Comparative analysis indicates that our proposed IDS outperforms state-of-the-art methods in terms of detection accuracy and efficiency. These results underscore the potential of integrating advanced DL models into IDS frameworks, thereby providing a scalable and effective solution to secure IoT networks against evolving DDoS threats. Future work will explore further enhancements, including the use of deeper Transformer architectures and cross-dataset validation, to ensure robustness in real-world deployments.
Keywords: Internet of Things security; Distributed Denial of Service; Intrusion Detection System; Deep Learning; Convolutional Neural Network; Transformer; Synthetic Minority Over-sampling Technique; anomaly detection Internet of Things security; Distributed Denial of Service; Intrusion Detection System; Deep Learning; Convolutional Neural Network; Transformer; Synthetic Minority Over-sampling Technique; anomaly detection

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MDPI and ACS Style

Wahab, S.A.; Sultana, S.; Tariq, N.; Mujahid, M.; Khan, J.A.; Mylonas, A. A Multi-Class Intrusion Detection System for DDoS Attacks in IoT Networks Using Deep Learning and Transformers. Sensors 2025, 25, 4845. https://doi.org/10.3390/s25154845

AMA Style

Wahab SA, Sultana S, Tariq N, Mujahid M, Khan JA, Mylonas A. A Multi-Class Intrusion Detection System for DDoS Attacks in IoT Networks Using Deep Learning and Transformers. Sensors. 2025; 25(15):4845. https://doi.org/10.3390/s25154845

Chicago/Turabian Style

Wahab, Sheikh Abdul, Saira Sultana, Noshina Tariq, Maleeha Mujahid, Javed Ali Khan, and Alexios Mylonas. 2025. "A Multi-Class Intrusion Detection System for DDoS Attacks in IoT Networks Using Deep Learning and Transformers" Sensors 25, no. 15: 4845. https://doi.org/10.3390/s25154845

APA Style

Wahab, S. A., Sultana, S., Tariq, N., Mujahid, M., Khan, J. A., & Mylonas, A. (2025). A Multi-Class Intrusion Detection System for DDoS Attacks in IoT Networks Using Deep Learning and Transformers. Sensors, 25(15), 4845. https://doi.org/10.3390/s25154845

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