Intelligent Intrusion Detection System in Internet of Vehicles using Random Forest, Logistic Regression and Decision Tree Algorithm

Abstract

This study introduces an Intelligent Intrusion Detection System (IDS) for Internet of Vehicles (IoV) security. Using Random Forest, Logistic Regression, and Decision Tree algorithms, it detects and prevents intrusions in real-time. By combining these algorithms, the IDS aims to improve accuracy and adapt to evolving threats. Evaluation metrics include accuracy (93.6%), precision (94.4%), recall (95.3%), and F1 score (96.7%). Analysis of false positives/negatives informs refinement for real-world use. The IDS enhances cybersecurity for connected vehicles. This IDS provides a robust defense against cyber threats in the IoV ecosystem, ensuring user safety and privacy. Its multi-algorithmic approach and high-performance metrics make it a reliable solution for safeguarding connected vehicles.