162124 20251017144628.0 doi 10.3390/fi17060230 sideral 144718 ART-2025-144718 eng Seyedkolaei, Ali Abdi A Deep Learning Approach for Multiclass Attack Classification in IoT and IIoT Networks Using Convolutional Neural Networks 2025 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/ES/DGA/T31-20R info:eu-repo/grantAgreement/ES/MCINN/PID2022-136476OB-I00 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Mahmoudi, Fatemeh García, José Universidad de Zaragoza (orcid)0000-0001-9485-7678 5008 560 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Ingeniería Telemática 17, 6 (2025), 230 [21 pp.] FUTURE INTERNET 1999-5903 2282992 http://zaguan.unizar.es/record/162124/files/texto_completo.pdf Versión publicada 2563762 http://zaguan.unizar.es/record/162124/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:162124 articulos driver 2025-10-17-14:25:00 ARTICLE