A comparative study of traditional machine learning models and the KNN-KFSC method for optimizing anomaly detection in VANETs

Abstract

In this research, we conducted a comparative analysis of traditional machine learning techniques and the innovative K-nearest neighbors-K-fuzzy subspace clustering (KNN-KFSC) methodology to detect anomalies in vehicular ad hoc network (VANET) infrastructures. Our evaluation included models such as support vector machine (SVM), random forest (RF), logistic regression (LR), and KNN. The KNN-KFSC model demonstrated exceptional performance with an overall accuracy rate of 99% in handling densely contextual data. It consistently exhibited high accuracy, recall, and F1 score metrics, indicating its effectiveness in detecting a broad spectrum of anomalies across various types of attacks in VANETs. In contrast, the RF algorithm achieved an 89% accuracy rate, showcasing competency in specific domains but revealing limitations in others. Both LR and SVM models exhibited identical accuracy rates of 92%. While effective in identifying specific types of attackers, these models showed weaknesses, potentially due to overfitting or inadequate management of dataset complexity. The KNN-KFSC approach emerged as the most promising option for detecting anomalies in software-defined VANETs, evidenced by its superior performance in accuracy and precision. Our findings underscore the necessity of advanced intrusion detection system techniques and highlight the importance of model refinement to address data imbalances and improve anomaly detection in VANET systems.