IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 14; p. 6379
• Main Authors: Akram, Urooj, Sharif, Wareesa, Shahroz, Mobeen, Mushtaq, Muhammad Faheem, Aray, Daniel Gavilanes, Thompson, Ernesto Bautista, Diez, Isabel de la Torre, Djuraev, Sirojiddin, Ashraf, Imran
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13-07-2023; MDPI
• Subjects: Access control; Accuracy; Algorithms; Blockchain; Confidentiality; Data collection; Data security; Internet of Things; Machine learning; Medical equipment; Network security; Patients; Performance evaluation; Privacy; Protection and preservation; Sensors; Software; threat protection system; Threats; Virtual private networks; Germany; privacy; threat protection system; Internet of Things; machine learning; confidentiality
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23146379

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.