Detection of Sybil & DDoS attacks in VANET using intelligent technique

This paper proposes a novel fuzzy logic controller (FLC) based model referred to as FBM to detect both the DDoS and the Sybil attacks in VANET using an intelligent fuzzy logic controller. A VANET environment is created with different numbers of attackers to check the efficacy of the proposed model using veins, which is used for simulation with an event-based network simulator OMNeT++ and a road traffic simulator SUMO. Furthermore, the performance of the proposed model is compared with a few other machine learning techniques, such as logistic regression (LR), the decision tree (DT), random forest classifier (RF), and the support vector machine (SVM). The proposed FBM model yields better performance than the existing machine-learning techniques. The proposed FBM performs better than RF in accuracy by 0.10492%, precision by 0.08065%, recall by 0.02505%, and F1 score by 0.088243% for the DDoS scenario. It is also observed that the FBM performs better than DT in accuracy by 3.91486%, precision by 14.39994%, and F1 score by 3.819618% for the Sybil scenario. The margin of error for the proposed FBM is also estimated for 95% of the confidence interval and is found to be 0.021691 for the DDoS scenario.