eprintid: 8680 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/86/80 datestamp: 2024-04-30 19:33:10 lastmod: 2024-05-07 20:07:22 status_changed: 2024-04-30 19:33:10 type: article metadata_visibility: show creators_name: Akram, Urooj creators_name: Sharif, Wareesa creators_name: Shahroz, Mobeen creators_name: Mushtaq, Muhammad Faheem creators_name: Gavilanes Aray, Daniel creators_name: Bautista Thompson, Ernesto creators_name: Diez, Isabel de la Torre creators_name: Djuraev, Sirojiddin creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: daniel.gavilanes@uneatlantico.es creators_id: ernesto.bautista@unini.edu.mx creators_id: creators_id: creators_id: title: IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: none keywords: threat protection system; privacy; confidentiality; Internet of Things; machine learning abstract: An Internet of Things (IoT) network is prone to many ways of threatening individuals. IoT sensors are lightweight, lack complicated security protocols, and face threats to privacy and confidentiality. Hackers can attack the IoT network and access personal information and confidential data for blackmailing, and negatively manipulate data. This study aims to propose an IoT threat protection system (IoTTPS) to protect the IoT network from threats using an ensemble model RKSVM, comprising a random forest (RF), K nearest neighbor (KNN), and support vector machine (SVM) model. The software-defined networks (SDN)-based IoT network datasets such as KDD cup 99, NSL-KDD, and CICIDS are used for threat detection based on machine learning. The experimental phase is conducted by using a decision tree (DT), logistic regression (LR), Naive Bayes (NB), RF, SVM, gradient boosting machine (GBM), KNN, and the proposed ensemble RKSVM model. Furthermore, performance is optimized by adding a grid search hyperparameter optimization technique with K-Fold cross-validation. As well as the NSL-KDD dataset, two other datasets, KDD and CIC-IDS 2017, are used to validate the performance. Classification accuracies of 99.7%, 99.3%, 99.7%, and 97.8% are obtained for DoS, Probe, U2R, and R2L attacks using the proposed ensemble RKSVM model using grid search and cross-fold validation. Experimental results demonstrate the superior performance of the proposed model for IoT threat detection. date: 2023-07 publication: Sensors volume: 23 number: 14 pagerange: 6379 id_number: doi:10.3390/s23146379 refereed: TRUE issn: 1424-8220 official_url: http://doi.org/10.3390/s23146379 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés An Internet of Things (IoT) network is prone to many ways of threatening individuals. IoT sensors are lightweight, lack complicated security protocols, and face threats to privacy and confidentiality. Hackers can attack the IoT network and access personal information and confidential data for blackmailing, and negatively manipulate data. This study aims to propose an IoT threat protection system (IoTTPS) to protect the IoT network from threats using an ensemble model RKSVM, comprising a random forest (RF), K nearest neighbor (KNN), and support vector machine (SVM) model. The software-defined networks (SDN)-based IoT network datasets such as KDD cup 99, NSL-KDD, and CICIDS are used for threat detection based on machine learning. The experimental phase is conducted by using a decision tree (DT), logistic regression (LR), Naive Bayes (NB), RF, SVM, gradient boosting machine (GBM), KNN, and the proposed ensemble RKSVM model. Furthermore, performance is optimized by adding a grid search hyperparameter optimization technique with K-Fold cross-validation. As well as the NSL-KDD dataset, two other datasets, KDD and CIC-IDS 2017, are used to validate the performance. Classification accuracies of 99.7%, 99.3%, 99.7%, and 97.8% are obtained for DoS, Probe, U2R, and R2L attacks using the proposed ensemble RKSVM model using grid search and cross-fold validation. Experimental results demonstrate the superior performance of the proposed model for IoT threat detection. metadata Akram, Urooj; Sharif, Wareesa; Shahroz, Mobeen; Mushtaq, Muhammad Faheem; Gavilanes Aray, Daniel; Bautista Thompson, Ernesto; Diez, Isabel de la Torre; Djuraev, Sirojiddin y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, daniel.gavilanes@uneatlantico.es, ernesto.bautista@unini.edu.mx, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System. Sensors, 23 (14). p. 6379. ISSN 1424-8220