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A Deep Learning-Based Approach for Intrusion Detection in IoT Networks Using Convolutional Neural Networks

Authors

  • Agha Wafa Abbas

    School of Computing, Arden University, United Kingdom
    Author

  • Syed Atir Raza Shirazi

    Department of Applied Computing Technologies, Faculty of IT&CS, University of Central Punjab, Lahore, Pakistan
    Author

  • Awais Malik

    Deputy Head School of Computing, ICMS, Pakistan
    Author

  • Hassan Bashir

    Department of Computer Science, School of Systems and Technology, University of Management and Technology Lahore, Pakistan
    Author

  • Nafeesa Yousaf

    Department of Computer Science, School of Systems and Technology, University of Management and Technology Lahore, Pakistan
    Author

DOI:

https://doi.org/10.22581/0381

Keywords:

Convolutional Neural Network (CNN), Decision Tree, Internet of Things (IoT), Intrusion Detection System (IDS),, Network Security

Abstract

As the Internet of Things (IoT) proliferates, the requirement for efficient intrusion detection systems (IDS) becomes increasing. With the increasing rise of Internet of Things (IoT) devices, the need for efficient intrusion detection systems (IDS) has risen too. In this study, a deep learning approach to the IDS system based on Convolutional Neural Network (CNN) on the NSL-KDD dataset is evaluated. A CNN model was built, and we found that the model got 78.27% accuracy on the test set, 98.42% on the training set, and 98.76% on the validation set. A baseline model was built using a Decision Tree classifier which achieved 78% test accuracy. The results show that the new attack type, saint and mscan from the NSL-KDD test set, is difficult, makes the model overfit, and reduces the test performance. However, these issues have not been entirely resolved by dropout regularization and by placing weights on the classes. The shortcomings and vulnerabilities of deep learning in IoT security are explored, and future directions like hybrid learning methods and advanced feature selection are proposed.

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References

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Published

2026-10-01

Issue

Vol. 45 No. 4 (2026): October Issue

Section

Articles

License

Copyright (c) 2026 Mehran University Research Journal of Engineering and Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

A Deep Learning-Based Approach for Intrusion Detection in IoT Networks Using Convolutional Neural Networks. (2026). Mehran University Research Journal of Engineering and Technology, 45(4), 21-31. https://doi.org/10.22581/0381

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