ABSTRACT: This study presents a comparative analysis of machine learning models for threat detection in Internet of Things (IoT) devices using the CICIoT2023 dataset. We evaluate Logistic Regression, ...

This study and project proposes a method for classifying Distributed Denial of Service (DDoS) attacks using the Random Forest Algorithm, with a focus on determining measurement metrics for ...

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.

Implementing the Random Forest algorithm within the nDPI (nDPI) framework can enhance the classification of encrypted traffic, enabling more accurate detection of malicious patterns. Future ...

In the second part of the analysis, three machine learning models—Logistic Regression, Random Forest, and XGBoost—were implemented for predictive performance. Logistic Regression outperformed others ...

This multi-phase project identifies key satisfaction drivers and provides actionable insights to improve customer experience using statistical analysis and machine learning models, including logistic ...

The application of RF to ToF-SIMS imaging facilitates the classification of complex chemical compositions and the identification of features contributing to these classifications. This tutorial aims ...