IoT intrusion detection system based on machine learning and deep learning

Authors

  • karrar Majid Jasim University of Information Technology and Communications
  • Joolan Rokan Nayef University of Information Technology and Communications

DOI:

https://doi.org/10.25195/ijci.v51i1.552

Keywords:

IoT Security, Deep Learning, Machine Learning,, Intrusion Detection

Abstract

The proliferation of Internet of Things IoT devices has amplified cybersecurity challenges, necessitating robust Intrusion Detection Systems IDS to safeguard against threats such as botnets and Distributed Denial-of-Service DDoS attacks. This paper evaluates the performance of Machine Learning ML and Deep Learning DL models on two benchmark datasets, BoT-IoT and CIC-IDS2017, to develop efficient IDS. Among ML models, XGBoost demonstrated the best performance, achieving 99.99% accuracy on BoT-IoT and 99.91% on CIC-IDS2017 with superior computational efficiency. For DL, Convolutional Neural Networks CNNs achieved 99.99% accuracy on BoT-IoT and 99.61% on CIC-IDS2017 with preprocessing, highlighting the critical role of data preparation. These findings underline the effectiveness of advanced ML/DL models and preprocessing techniques in enhancing IoT security, providing a pathway for real-time, scalable intrusion detection in IoT environments.

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Author Biographies

karrar Majid Jasim, University of Information Technology and Communications

Informatics Institute for Postgraduate Studies

Joolan Rokan Nayef, University of Information Technology and Communications

Informatics Institute for Postgraduate Studies

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Published

2025-06-07