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Mahmoudi-Nasr, Payam, Mousavand, Mahdi. (1404). A Network Intrusion Detection System Based on Deep Learning Models in IoT Systems. پژوهشنامه مدیریت اجرایی, (), 1-11. doi: 10.22080/frai.2025.5664
Payam Mahmoudi-Nasr; Mahdi Mousavand. "A Network Intrusion Detection System Based on Deep Learning Models in IoT Systems". پژوهشنامه مدیریت اجرایی, , , 1404, 1-11. doi: 10.22080/frai.2025.5664
Mahmoudi-Nasr, Payam, Mousavand, Mahdi. (1404). 'A Network Intrusion Detection System Based on Deep Learning Models in IoT Systems', پژوهشنامه مدیریت اجرایی, (), pp. 1-11. doi: 10.22080/frai.2025.5664
Mahmoudi-Nasr, Payam, Mousavand, Mahdi. A Network Intrusion Detection System Based on Deep Learning Models in IoT Systems. پژوهشنامه مدیریت اجرایی, 1404; (): 1-11. doi: 10.22080/frai.2025.5664

A Network Intrusion Detection System Based on Deep Learning Models in IoT Systems

Future Research on AI and IoT
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 14 مرداد 1404    اصل مقاله (889.13 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22080/frai.2025.5664
نویسندگان
Payam Mahmoudi-Nasr؛ Mahdi Mousavand*
Department of Computer Engineering, University of Mazandaran, Mazandaran, Iran.
تاریخ دریافت: 12 تیر 1404،  تاریخ پذیرش: 14 مرداد 1404 
چکیده
The Internet of Things (IoT) plays a vital role in the life of people today. However, as the use of IoT devices becomes more widespread, there is a growing concern about security threats, like botnet attacks. Therefore, using inclusive solutions is required. Intrusion detection systems (IDS) can detect and mitigate attacks on IoT devices by analyzing network traffic and device behavior. This paper proposes an IDS that uses Deep Learning (DL) techniques. It is based on an ensemble learning model that employs diversity, and F1-Score as a performance metric, to select the best transfer learning models. It also proposes 20 individual and hybrid DL models, including Deep Neural Networks (DNN), Convolution Neural Networks (CNN), and Recurrent Neural Networks (RNNs), to detect and classify regular and botnet attack classes. The proposed IDS engages a feature engineering method to reduce unnecessary computation. The Bot-IoT dataset used in this paper contained DDoS, DoS, Reconnaissance, and theft attack labels. The proposed IDS was compared with existing methods using the Bot-IoT dataset. Experimental results reveal a high performance of the proposed model for detecting and classifying various attack and regular labels.
کلیدواژه‌ها
Internet of Things؛ Intrusion Detection System؛ Botnet Detection؛ Deep Learning؛ Feature Engineering
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