Neuro Fusion-CART: A Hybrid Intelligence Framework for Anomaly Detection in VANETs

Abstract

Vehicular Ad Hoc Network (VANET) generate large volumes of real-time data such as packet size, latency, vehicle speed, and signal strength, which can be vulnerable to malicious attacks. Ensuring secure and reliable communication is therefore a critical challenge. Traditionally, anomaly detection in such networks relied on rule-based systems and statistical threshold techniques. These methods depended heavily on predefined rules and manual monitoring, making them less effective in handling complex, dynamic traffic patterns. They often failed to detect unknown or evolving attack behaviors and lacked adaptability to real-time scenarios. In the proposed system, a Machine Learning (ML) and Deep Learning (DL) – Classification and Regression Trees (CART) based hybrid approach is implemented to improve detection accuracy and adaptability. The system utilizes multiple algorithms including Linear Regression (LR), Decision Tree (DT), Passive Aggressive (PA) algorithms, and a novel proposed model called KNeuroFusion CART. This hybrid model integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for feature extraction, combined with K-Nearest Neighbors (KNN) for final classification and regression. The models are trained on VANET dataset features such as packet rate, vehicle speed, and anomaly score, achieving high accuracy in detecting normal and attack patterns. The research is developed using Flask for web deployment, SQLite for database management, and libraries like Scikit-learn, TensorFlow, Pandas, and Matplotlib. The system provides both single and batch prediction capabilities, along with visualization and analysis tools