000162124 001__ 162124
000162124 005__ 20251017144628.0
000162124 0247_ $$2doi$$a10.3390/fi17060230
000162124 0248_ $$2sideral$$a144718
000162124 037__ $$aART-2025-144718
000162124 041__ $$aeng
000162124 100__ $$aSeyedkolaei, Ali Abdi
000162124 245__ $$aA Deep Learning Approach for Multiclass Attack Classification in IoT and IIoT Networks Using Convolutional Neural Networks
000162124 260__ $$c2025
000162124 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162124 5203_ $$aThe 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.
000162124 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T31-20R$$9info:eu-repo/grantAgreement/ES/MCINN/PID2022-136476OB-I00
000162124 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000162124 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000162124 700__ $$aMahmoudi, Fatemeh
000162124 700__ $$0(orcid)0000-0001-9485-7678$$aGarcía, José$$uUniversidad de Zaragoza
000162124 7102_ $$15008$$2560$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Ingeniería Telemática
000162124 773__ $$g17, 6 (2025), 230 [21 pp.]$$tFUTURE INTERNET$$x1999-5903
000162124 8564_ $$s2282992$$uhttps://zaguan.unizar.es/record/162124/files/texto_completo.pdf$$yVersión publicada
000162124 8564_ $$s2563762$$uhttps://zaguan.unizar.es/record/162124/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000162124 909CO $$ooai:zaguan.unizar.es:162124$$particulos$$pdriver
000162124 951__ $$a2025-10-17-14:25:00
000162124 980__ $$aARTICLE