A Deep Learning based Misbehaviour Detection using Blockchain in SDN based 5G-VANET
Keywords:
Quantum Key Distribution, Hybrid misbehaviour detection, Similarity based message verification, Trust management, Deep learningAbstract
Vehicular ad hoc network (VANETs) is fabricated by adopting the principles of ad hoc manner that embraces of group of vehicle in mobility or stationary mode connected by wireless network. Main intention of VANET is to accommodate safety and comfort in vehicular environments through information sharing. Due to highly dynamic connections and sensitive data sharing, the VANET is excessively prone to attacks and being a wireless network it is an eye-catching environment for attackers. To overcome this attack, we have proposed deep learning entrenched misbehavior detection using blockchain technology. Furthermore, to enhance the efficiency of communication and security in VANET, the software defined network is integrated with VANET known as SDVANET. In this paper, there are three phases such as quantum based authentication, dynamic clustering and hybrid misbehavior detection. Initially, the vehicles are registered and authenticated through blockchain using Quantum Key Distribution (QKD) which ensures the vehicle legitimacy. Here, the blockchain is adopted for secure communication and data storage that enhance data privacy. Following the vehicles are clustered by utilizing K-means algorithm with Leader Optimization Algorithm (i.e. Leader based K-means clustering (LKCM)) for optimal centroid selection and 5G communication is utilized, thus improves communication efficiency thereby minimizing latency. After that, hybrid misbehavior detection is accomplished in terms of data-centric and node-centric misbehavior detection. Data-centric misbehavior detection is performed by trust estimation of each vehicle in direct and indirect manner using Multi-head Attention Long Term Short Memory (MHA-LSTM). At last, we execute node-centric based misbehavior detection by determining the similarity based message verification using Fuzzy Similarity which are result in intensifying VANET security. The proposed work is conducted by OMNeT++ and several performance metrics are evaluated in terms of end-to-end delay, packet loss ratio, packet delivery ratio, transmission overhead, throughput and accuracy where the proposed outperforms the existing approaches.
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