A Deep Learning Approach for Multiclass Attack Classification in IoT and IIoT Networks Using Convolutional Neural Networks

Ali Abdi Seyedkolaei (), Fatemeh Mahmoudi and José García ()
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Ali Abdi Seyedkolaei: Department of Computer Engineering, University of Mazandaran (UMZ), Babolsar 47416-13534, Iran
Fatemeh Mahmoudi: Computer Engineering Faculty of Engineering & Technology, University of Mazandaran (UMZ), Babolsar 47416-13534, Iran
José García: Aragon Engineering Research Institute (I3A), School of Engineering and Architecture (EINA), University of Zaragoza (UINZAR), 50018 Zaragoza, Spain

Future Internet, 2025, vol. 17, issue 6, 1-21

Abstract: The rapid expansion of the Internet of Things (IoT) and industrial Internet of Things (IIoT) ecosystems has introduced new security challenges, particularly the need for robust intrusion detection systems (IDSs) capable of adapting to increasingly sophisticated cyberattacks. In this study, we propose a novel intrusion detection approach based on convolutional neural networks (CNNs), designed to automatically extract spatial patterns from network traffic data. Leveraging the DNN-EdgeIIoT dataset, which includes a wide range of attack types and traffic scenarios, we conduct comprehensive experiments to compare the CNN-based model against traditional machine learning techniques, including decision trees, random forests, support vector machines, and K-nearest neighbors. Our approach consistently outperforms baseline models across multiple performance metrics—such as F1 score, precision, and recall—in both binary (benign vs. attack) and multiclass settings (6-class and 15-class classification). The CNN model achieves F1 scores of 1.00, 0.994, and 0.946, respectively, highlighting its strong generalization ability across diverse attack categories. These results demonstrate the effectiveness of deep-learning-based IDSs in enhancing the security posture of IoT and IIoT infrastructures, paving the way for intelligent, adaptive, and scalable threat detection systems.

Keywords: intrusion detection system (IDS); Internet of Things (IoT); industrial IoT (IIoT); deep learning; convolutional neural networks (CNN); network security (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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