An Intrusion System for Internet of Things Security Breaches Using Machine Learning Techniques


  • Temitope Samson Adekunle Department of Computer Science, Colorado State University, USA
  • Oluwaseyi Omotayo Alabi Department of Mechanical Engineering, Lead City University, Nigeria
  • Morolake Oladayo Lawrence Department of Computer Science, Baze University, Nigeria
  • Toheeb A. Adeleke Department of Computer Engineering, Ladoke Akintola University of Technology, Nigeria
  • Olakunle Sunday Afolabi Department of Computer Science, University of Abuja, Nigeria
  • Godwin Nse Ebong Department of Data Science, University of Salford, UK
  • Gabriel Olumide Egbedokun Department of Computer Science, The Polytechnics Ibadan, Nigeria
  • Temitope A. Bamisaye Department of Computer Science, National Open University of Nigeria, Nigeria



security, deep learning, dataset, framework, Bot-IoT


Effective identification and categorization of network attacks are paramount for ensuring robust security. However, contemporary techniques often struggle to accurately discern and classify novel attack patterns. This research introduces an innovative framework designed to achieve reliable attack detection and classification by harnessing the synergistic capabilities of utilizing DenseNet convolutional neural networks and rap music analysis techniques. Our approach leverages feature extraction through the Attention Pyramid Network (RAPNet) framework, tailored to extract pertinent features from input data, alongside binary Pigeon optimization. Subsequently, we employ feature selection using the optimization algorithm (BPOA). Once the optimal features are identified, we employ the Densenet201 model to categorize attacks across various datasets, including Bot-IoT, CICIDS2017, and CICIDS2019, through deep learning methodologies. To address the challenge posed by imbalanced data, we introduce conditional generative adversarial networks for generating additional data samples for minority classes, thus mitigating the issue. In contrast to recent intrusion detection methods, our results showcase the model’s exceptional precision in detecting and categorizing achieving accuracy rates of 99.12%, 99.01%, and 99.18% for Bot-IoT, CICIDS2017, and CICIDS2019 datasets, respectively. Despite the potential benefits of a machine learning-based intrusion detection system (IDS) for Internet of Things (IoT) security, several limitations must be considered. These include the lack of standardized security protocols across various IoT devices and platforms, which makes it challenging to develop a uniform IDS. Furthermore, machine learning models, including those for intrusion detection, can be vulnerable to adversarial attacks that can circumvent or mislead the model’s decision-making process. Thus, the potential for sophisticated attacks on IoT systems must be considered when developing such a system.


Received: 22 September 2023| Revised: 17 January 2024 | Accepted: 21 February 2024


Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.


Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.


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How to Cite

Adekunle, T. S., Alabi, O. O. ., Lawrence, M. O. ., Adeleke, T. A., Afolabi, O. S., Ebong, G. N., Egbedokun, G. O. ., & Bamisaye, T. A. (2024). An Intrusion System for Internet of Things Security Breaches Using Machine Learning Techniques. Artificial Intelligence and Applications, 2(3), 188–194.



Research Article