Data Classification Model for COVID-19 Pandemic

Folorunso, Sakinat Oluwabukonla; Awotunde, Joseph Bamidele; Adeboye, Nureni Olawale; Matiluko, Opeyemi Emmanuel

doi:10.1007/978-3-030-77302-1_6

Cited by 10 publications

(1 citation statement)

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“…Many different performance indicators were used to assess the performance and effectiveness of ML models on the different datasets. Some commonly used indicators, which will also be adopted for this study, are confusion matrix, ROC_AUC, F1_score, recall, precision, and accuracy [60]. The confusion matrix is a table shown in Table 3, representing the detection rate of classes of dataset, thereby measuring the performance of an ML model on the test data.…”

Section: Performance Indicatorsmentioning

confidence: 99%

An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks

et al. 2023

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With less human involvement, the Industrial Internet of Things (IIoT) connects billions of heterogeneous and self-organized smart sensors and devices. Recently, IIoT-based technologies are now widely employed to enhance the user experience across numerous application domains. However, heterogeneity in the node source poses security concerns affecting the IIoT system, and due to device vulnerabilities, IIoT has encountered several attacks. Therefore, security features, such as encryption, authorization control, and verification, have been applied in IIoT networks to secure network nodes and devices. However, the requisite machine learning models require some time to detect assaults because of the diverse IIoT network traffic properties. Therefore, this study proposes ensemble models enabled with a feature selection classifier for Intrusion Detection in the IIoT network. The Chi-Square Statistical method was used for feature selection, and various ensemble classifiers, such as eXtreme gradient boosting (XGBoost), Bagging, extra trees (ET), random forest (RF), and AdaBoost can be used for the detection of intrusion applied to the Telemetry data of the TON_IoT datasets. The performance of these models is appraised based on accuracy, recall, precision, F1-score, and confusion matrix. The results indicate that the XGBoost ensemble showed superior performance with the highest accuracy over other models across the datasets in detecting and classifying IIoT attacks.

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Section: Performance Indicatorsmentioning

confidence: 99%