IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System
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 blackm...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/14/6379 |
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| author | Urooj Akram Wareesa Sharif Mobeen Shahroz Muhammad Faheem Mushtaq Daniel Gavilanes Aray Ernesto Bautista Thompson Isabel de la Torre Diez Sirojiddin Djuraev Imran Ashraf |
| author_facet | Urooj Akram Wareesa Sharif Mobeen Shahroz Muhammad Faheem Mushtaq Daniel Gavilanes Aray Ernesto Bautista Thompson Isabel de la Torre Diez Sirojiddin Djuraev Imran Ashraf |
| author_sort | Urooj Akram |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T00:39:48Z |
| format | Article |
| id | doaj.art-2a6fa14a834f40758fdae4954f181602 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:39:48Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2a6fa14a834f40758fdae4954f1816022023-11-18T21:16:55ZengMDPI AGSensors1424-82202023-07-012314637910.3390/s23146379IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection SystemUrooj Akram0Wareesa Sharif1Mobeen Shahroz2Muhammad Faheem Mushtaq3Daniel Gavilanes Aray4Ernesto Bautista Thompson5Isabel de la Torre Diez6Sirojiddin Djuraev7Imran Ashraf8Department of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, PakistanDepartment of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, PakistanDepartment of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, PakistanDepartment of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur 63100, Punjab, PakistanHigher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, SpainHigher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, SpainDepartment of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belén, 15, 47011 Valladolid, SpainDepartment of Software Engineering, New Uzbekistan University, Tashkent 100007, UzbekistanDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaAn 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.https://www.mdpi.com/1424-8220/23/14/6379threat protection systemprivacyconfidentialityInternet of Thingsmachine learning |
| spellingShingle | Urooj Akram Wareesa Sharif Mobeen Shahroz Muhammad Faheem Mushtaq Daniel Gavilanes Aray Ernesto Bautista Thompson Isabel de la Torre Diez Sirojiddin Djuraev Imran Ashraf IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System Sensors threat protection system privacy confidentiality Internet of Things machine learning |
| title | IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System |
| title_full | IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System |
| title_fullStr | IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System |
| title_full_unstemmed | IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System |
| title_short | IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System |
| title_sort | iottps ensemble rksvm model based internet of things threat protection system |
| topic | threat protection system privacy confidentiality Internet of Things machine learning |
| url | https://www.mdpi.com/1424-8220/23/14/6379 |
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