A Comparative Analysis of a Novel Security Framework for Misbehaviour Detection in Vehicular Ad Hoc Networks
Keywords:
Artificial Neural Network, Genetic Algorithm, Hybrid Detection, Misbehavior Detection, Security in VANETs, Vehicular ad hoc NetworksAbstract
This research introduces a novel Security Framework for Misbehaviour Detection (SFMD) in Vehicular Ad Hoc Networks (VANETs) and presents a comparative analysis of the proposed framework. Leveraging a hybrid approach with Genetic Algorithms (GAs) and Deep Learning (DL), SFMD addresses the critical need for robust security in VANETs. The novelty in this research study is the use of Genetic Algorithms in misbehaviour detection in VANETs. Traditionally, the complexity of defining a suitable fitness function for GAs in this context has deterred their application. However, SFMD overcomes this challenge by introducing an innovative solution: employing an Artificial Neural Network (ANN) as the fitness function for GAs. This paradigm shift opens new avenues for efficient and effective feature selection, marking the first instance in VANET research where GA plays a pivotal role in misbehaviour detection. The synergy between these two cutting-edge approaches, coupled with the integration of contextual data and the utilization of an ANN-based fitness function in GA, equips SFMD to address the unique challenges posed by VANETs, where rapid decision-making and adaptability are paramount. By integrating contextual data, including vehicle positions, speed, and communication patterns, SFMD utilizes GAs for feature selection and DL for real-time misbehaviour detection. The 10- fold CV used enabled the whole system to be unbiased, achieving precision, recall, and F1 scores of 0.9999 in binary classification and 0.9976, 0.9977, and 0.9977 in multiclass classification respectively. Comparative analysis with recent works underscores SFMD's superiority, highlighting its potential to enhance the security landscape of VANETs. The study emphasizes the importance of context awareness, paving the way for future real-world validations and large-scale experiments. Future research can explore SFMD's practicality in diverse VANET scenarios, validating its effectiveness. However, limitations include the dependence on simulated datasets and the need for real-world deployment to uncover potential challenges.
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