Enhanced intrusion detection in iot with a novel prbf kernel and cloud integration

HIGHLIGHTS

• What: This study compares the proposed PRBF-SVM with Logistic Regression SVM and XGBoost models optimized through rigorous hyperparameter tuning to demonstrate significant improvements in detection rates. This study investigates a comprehensive cloud-integrated ML framework for intrusion detection, using Logistic Regression (LR), Support Vector Machines (SVMs), and XGBoost models to analyze traffic patterns and detect anomalies at the device and network levels . The focus of the analysis was on operational factors, including CPU processing time without GPU involvement, and model size in memory.
• Who: gerry from the Department of Computer Science and Engineering, Koneru . . .
  

   

  

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