IoV Vulnerability Classification Algorithm Based on Knowledge Graph

Abstract

With the rapid development of smart technologies, the Internet of Vehicles (IoV) is revolutionizing transportation and mobility. However, the complexity and interconnectedness of IoV systems lead to a growing number of security incidents caused by vulnerabilities. Current vulnerability classification algorithms often struggle to address the low occurrence frequency and incomplete information associated with IoV vulnerabilities, resulting in decreased precision and recall rates of classifiers. To address these challenges, an effective vulnerability classification algorithm (KG-KNN), is proposed, designed to handle imbalanced sample data. KG-KNN integrates the vulnerability information of IoV and the association relationship between features by constructing a feature knowledge graph to form a complete knowledge system. It adds the correlation relationship between features to the similarity calculation, calculates vulnerability similarity from multiple dimensions, and improves the prediction performance of the classifier. The experimental results show that compared to the k-NearestNeighbor (KNN), Support Vector Machine (SVM), Deep Nueral Network (DNN) and TFI-DNN classification algorithms, KG-KNN can effectively deal with imbalanced sample data and has different degrees of improvement in precision, recall, and the F1 score.