An Efficient IoT Intrusion Detection System Based on Machine Learning Approaches
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Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
American Institute of Physics
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The Internet of Things (IoT) has advanced quickly and has been integrated into many different fields. With the use of this technology, gadgets have the ability for sending, receiving, and processing data automatically. IoT was rapidly accepted in many important fields since it makes life easier and boosts service quality, but privacy and security concerns are still significant problems. Intrusion Detection System (IDS) could be used as a security feature to protect IoT networks from a variety of cyber-attacks, which is a relief. This study suggests the utilization of the IDS for defending against various cyber-attacks in IoT systems. The suggested approach makes use of Random Forest (RF), Multi-layer Perceptron (MLP) to increase the detection rate, we use the pipeline to put together some processes that may be cross-validated against one another. A contemporary dataset was utilized for assessing and analyzing the performance results for validating the effectiveness of the suggested IDS approach. The evaluation findings show that the suggested IDS method may greatly increase detection performance results concerning accuracy rate while also improving detection efficiency. F1-Score, Recall, and Precision, The performance metrics demonstrate that the suggested approach produces significant outcomes, particularly when employing the pipeline with all dataset features, where the model achieved a very high result of 95.13 %.
Açıklama
Article number : 030002
Volume editors : Albaker B.M., Ali R.M., Kwad A.M.
Conference name : International Research Conference on Engineering and Applied Sciences 2023, IRCEAS 2023
Conference code : 208810
Anahtar Kelimeler
Cybersecurity, Intrusion detection system, Machine learning, Network security
Kaynak
AIP Conference Proceedings
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
3211
Sayı
1
Künye
Jasim, A. A., Hazim, L. R., Ata, O., & Ilyas, M. (2025). An efficient IoT intrusion detection system based on machine learning approaches. In AIP Conference Proceedings, 3211(1). AIP Publishing LLC. 10.1063/5.0257268
