A hybrid deep learning model for detection and mitigation of DDoS attacks in VANETs

Intelligent transport systems are increasing in application for real-time communication between vehicles and the infrastructure, and along with that are increasing the popularity of vehicular ad-hoc networks (VANETs). However, the very open and dynamic environment gives rise to varied kinds of DDoS attacks that can disrupt safety-critical services. The existing mechanisms for detection of DDoS attacks in VANETs have been found to suffer from low efficacy of detection, high magnitude of false alarm rates, and poor adaptability to evolving patterns of attacks. To address this challenge, this paper introduces VANET-DDoSNet++, a novel, multi-layered defense framework that uniquely integrates optimized feature selection, advanced deep learning detection, adaptive reinforcement learning mitigation, and secure blockchain-based reporting. The preprocessing step ensures high quality of data by dealing with missing values, removing outliers, augmenting the data, and detecting outliers effectively, preparing for analysis. The features including network traffic statistics, spatiotemporal data, deep traffic embeddings, and behavioural patterns are extracted. To improve the detection performance, a hybrid selection strategy is introduced featuring an adaptive dragonfly algorithm (ADA) and an Enhanced grasshopper optimization algorithm (EGOA) for feature selection where the optimal features are determined. Finally, the detection part applies a hybrid architecture of deep learning referred to as VANET-DDoSNet++, where convolutional LSTM networks, attention layers, and residual/dense connections are used for reliable DDoS detection. An adaptive reinforcement learning-based intrusion mitigation approach with reward shaping tailors defense strategies dynamically with evolving attack vectors by all means. The decentralized trust management mechanism based on blockchain is intended for a secure and verifiable real-time threat reporting from vehicles. The CIC-DDoS2019 dataset, which includes real-world vehicular traffic data with modern reflective DDoS attacks, is utilized for evaluation. The experimental results show that VANET-DDoSNet++ surpasses other currently existing methodologies achieving 98.04% accuracy with 70% training data and 99.18% with 80% training data besides dramatically reducing false positive and negative rates as well as improving overall precision, F1-score, sensitivity, and specificity. The factor deals with the evolution of DDoS attacks whereas VANET networks offer a dynamic and secure intrusion detection and mitigation framework.