Jasim, Abdulrahman AhmedHazim, Layth RafeaAta, OğuzIlyas, Muhammad2025-08-142025-08-142025Jasim, 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. 10.1063/5.02572680094-243Xhttps://hdl.handle.net/20.500.12939/5913Article 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 : 208810The 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 %.eninfo:eu-repo/semantics/closedAccessCybersecurityIntrusion detection systemMachine learningNetwork securityConference Object10.1063/5.0257268321112-s2.0-105005946907Q4