A Lightweight Python-Based Framework for Detecting False Information Attacks in Vehicular Ad Hoc Networks Using Isolation Forest

Abstract

Vehicular Ad Hoc Networks (VANETs) play a critical role in Intelligent Transportation Systems (ITS) by enabling real-time vehicle communication for safety and traffic management. However, the open and decentralized nature of VANETs makes them vulnerable to False Information Attacks (FIA), where malicious vehicles disseminate fabricated data such as fake congestion alerts or incorrect speed information. This paper presents a lightweight and infrastructure-free framework for detecting FIA using an unsupervised machine learning approach based on the Isolation Forest algorithm. Unlike existing methods that require roadside units (RSUs), labeled datasets, or computationally intensive network simulators, the proposed framework operates using a small set of behavioral features extracted from vehicle beacon messages. The system is implemented entirely in Python and evaluated on a synthetically generated dataset designed to emulate realistic VANET conditions. Experimental results demonstrate an accuracy of 94.2%, precision of 86.1%, recall of 77.5%, and an F 1 -score of 81.6%. The results show that the proposed framework achieves competitive detection performance while maintaining low computational overhead, making it suitable for deployment on resource-constrained onboard units (OBUs) in real-world vehicular networks.