Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things

Abstract

In the context of 6G technology, the Internet of Everything aims to create a vast network that connects both humans and devices across multiple dimensions. The integration of smart healthcare, agriculture, transportation, and homes is incredibly appealing, as it allows people to effortlessly control their environment through touch or voice commands. Consequently, with the increase in Internet connectivity, the security risk also rises. However, the future is centered on a six-fold increase in connectivity, necessitating the development of stronger security measures to handle the rapidly expanding concept of IoT-enabled metaverse connections. Various types of attacks, often orchestrated using botnets, pose a threat to the performance of IoT-enabled networks. Detecting anomalies within these networks is crucial for safeguarding applications from potentially disastrous consequences. The voting classifier is a machine learning (ML) model known for its effectiveness as it capitalizes on the strengths of individual ML models and has the potential to improve overall predictive performance. In this research, we proposed a novel classification technique based on the DRX approach that combines the advantages of the Decision tree, Random forest, and XGBoost algorithms. This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems. Our experiments were conducted using the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets. The findings of our study show that the DRX-based technique works better than the others. It achieved a higher accuracy of 99.88% on the NSL-KDD dataset, 99.93% on the UNSW-NB15 dataset, and 99.98% on the CIC-IDS2017 dataset, outperforming the other methods. Additionally, there is a notable reduction in the false positive rates to 0.003, 0.001, and 0.00012 for the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets.

A Review on Mitigating Privacy Risks in IoT-Enabled Smart Homes

“…Furthermore, we compared the performance and security properties of our proposed protocol with those of existing protocols. Our results demonstrate that our protocol offers better security while maintaining lower computational costs, making it suitable for practical IoT-based smart-home environments [47]. The primary goal of IoT security is to safeguard privacy, confidentiality, and the overall security of users, infrastructure, data, and devices within the IoT network, while also ensuring the availability of services offered by the IoT ecosystem.…”

Text Mining Algorithm Naive Bayes Classifier to Improve Quality Sentiment Analysis Passport Mobile Application

“…The transformation stage of the KDD method can be run as a model representation, this model representation is made from data that is still in the form of sentences that are still arranged in the form of words into numbers so that they can be processed and calculated (Farooqi et al, 2023). Each review will be assigned a sentiment label based on the sentiment score that has been obtained.…”

Estimating Accident Reduction Rate after Maritime Traffic Safety Assessment Using Synthetic Minority Oversampling Technique and Machine Learning Algorithm

“…They applied the SMOTE technique to address the class imbalance issue in basic data. This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems [27].…”