A Robust Authentication and Trust Detection With Privacy Preservation of Data for Fog Computing in VANET Using Adaptive Deep Neural Network

• Published in: IEEE access 2024, Vol.12, p.161227-161246
• Main Authors: Jia, Jia, Kumarasamy, Sathiya Sekar, Pokkuluri, Kiran Sree, Kumar, K. Suresh, Priyanka, Thella Preethi, Wang, Feng
• Format: Article
• Language: English
• Subjects: adaptive deep neural networks; Algorithms; Artificial neural networks; Authentication; Automobiles; Computational modeling; Cybersecurity; Data privacy; Deep learning; Edge computing; enhanced garter snake optimization algorithm; Mobile ad hoc networks; Neural networks; node authentication; Nodes; optimal key-based data sanitization; Optimization; Privacy; Safety; Safety critical; Security; trust detection; Vehicles; Vehicular ad hoc networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3486811

Trust detection and node authentication within fog computing for the Vehicle Ad-hoc Networks (VANETs) are used to determine whether the automobiles and other infrastructure elements including Roadside Units (RSUs) are valid and secure before permitting them to function on the system. It is essential for ensuring the privacy and authenticity of automobile networks, particularly for systems that require safety-critical communication. However, the distributed and decentralized design of fog computing increases difficulties in establishing security measures and coordination between various nodes that are responsible for trust detection and authentication. In fog-enabled VANETs, trust and privacy remain a key challenge. To overcome the existing challenges, a new system for node authentication and trust detection in fog computing for VANETs is developed. Initially, node authentication and trust detection in vehicular networks is conducted using Adaptive Deep Neural Networks (ADNN). Verification of the node's authenticity and evaluating its trust scores will considerably minimize the chance of cyber attacks and fraudulent behavior across the system, thus enhancing the security of the entire system. Node authentication on the VANET model promotes safe interaction between vehicles. Trust detection in VANET guarantees the integrity of information transferred between vehicles. The parameters of the ADNN are optimally tuned with the help of an Enhanced Garter Snake Optimization Algorithm (EGSOA) to enhance the performance. Some of the models focus only on node authentication and do not consider privacy issues. Thus, it affects the users' identities and personal information. So, in our model after completing the node authentication and trust detection, privacy preservation of data is performed using Optimal Key-aided Data Sanitization (OPDS). Here, the same EGSOA strategy is employed to get the sanitized key to increase security. The effectiveness of this newly developed fog computing framework for VANETs is evaluated against traditional models, with an expectation of achieving superior accuracy.