Detecting Greedy Behaviour in TDMA-Based VANETs Using Watchdog and SVM

Vehicular Ad-Hoc Networks (VANETs) encounter various security threats, such as greedy behaviour attacks, with most existing research focusing on the CSMA/CD protocol. This paper investigates the TDMA protocol, specifically Distributed Time Division Multiple Access (DTMAC). In this paper, we focused on identifying and addressing four novel types of greedy actions that attackers can take advantage of, revealing vulnerabilities that have not been investigated before. To detect these behaviours, we propose a watchdog model designed to analyse network traffic, extract relevant features, and generate datasets at varying levels of network density. We use Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel to identify attackers in the network, employing Grid Search Cross-Validation (GSCV) for optimal results. The effectiveness of our proposed solution is evaluated through in-depth simulations using the NS2 simulator and Python. The results show that the proposed detection method can achieve a high detection rate with an accuracy attaining 95% in low density scenario and 80 % in high density scenario.