DDoS attack detection in intelligent transport systems using adaptive neuro-fuzzy inference system

An intelligent transportation system consists of a variety of applications that analyze and exchange information to reduce traffic, enhance traffic management, lessen the impact on the environment, and boost the advantages of transportation for both business users and the general public. Moreover, Intelligent Transportation Systems is different from the standard vehicular ad hoc network design since it functions in a highly dynamic environment brought on by the quick mobility between the nodes in short connection times. These traits make various threats, weaknesses, and denial-of-service assaults possible. The protection of the intelligent transportation system from attacks and continual maintenance is crucial. In this research work, a Distributed Denial of Service attack detection scheme is proposed to protect the Intelligent Transportation System ecosystem, making use of the Adaptive Neuro-Fuzzy Inference System. By resolving the flaws in the DDoS attack detection methods that are currently in use, the security needs of the Intelligent Transportation Systems ecosystem are taken into account. The learning approach of artificial neural networks and the fuzzy logic model is integrated into the Fuzzy System. Based on the experimental results, the proposed model achieved 94.3% accuracy, outperforming traditional classifiers such as Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Convolutional Neural Network. The system demonstrated low false positive rates and high detection reliability, ensuring suitability for real-world Intelligent Transportation Systems security. The proposed scheme attained better results in terms of accuracy, precision, recall, and F1 score.