Labels Predicted by AI
Model Ensemble Evaluation Metrics Role of Machine Learning
Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.
Abstract
Network security threats in embedded systems pose significant challenges to critical infrastructure protection. This paper presents a comprehensive framework combining ensemble learning methods with explainable artificial intelligence (XAI) techniques for robust anomaly detection in network traffic. We evaluate multiple machine learning models including Random Forest, Gradient Boosting, Support Vector Machines, and ensemble methods on a real-world network traffic dataset containing 19 features derived from packet-level and frequency domain characteristics. Our experimental results demonstrate that ensemble methods achieve superior performance, with Random Forest attaining 90