A Robust Authentication and Trust Detection With Privacy Preservation of Data for Fog Computing in VANET Using Adaptive Deep Neural Network
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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10736612/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850198679884922880 |
|---|---|
| author | Jia Jia Sathiya Sekar Kumarasamy Kiran Sree Pokkuluri K. Suresh Kumar Thella Preethi Priyanka Feng Wang |
| author_facet | Jia Jia Sathiya Sekar Kumarasamy Kiran Sree Pokkuluri K. Suresh Kumar Thella Preethi Priyanka Feng Wang |
| author_sort | Jia Jia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9ef653e792ea4862b4559b0d6d00ea69 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9ef653e792ea4862b4559b0d6d00ea692025-08-20T02:12:49ZengIEEEIEEE Access2169-35362024-01-011216122716124610.1109/ACCESS.2024.348681110736612A Robust Authentication and Trust Detection With Privacy Preservation of Data for Fog Computing in VANET Using Adaptive Deep Neural NetworkJia Jia0Sathiya Sekar Kumarasamy1Kiran Sree Pokkuluri2https://orcid.org/0000-0001-8601-4304K. Suresh Kumar3Thella Preethi Priyanka4Feng Wang5https://orcid.org/0009-0009-4368-1321School of Artificial Intelligence, Dongguan City University, Dongguan, Guangdong, ChinaDepartment of Electrical and Electronics Engineering, K. S. R. College of Engineering, Tiruchengode, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, IndiaDepartment of Information Technology, Saveetha Engineering College (Autonomous), Thandalam, Chennai, Tamil Nadu, IndiaDepartment of Computer Science Engineering, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur, Andhra Pradesh, IndiaSchool of Economics and Management, Hainan Normal University, Haikou, ChinaTrust 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.https://ieeexplore.ieee.org/document/10736612/Vehicular ad-hoc networksnode authenticationtrust detectionadaptive deep neural networksoptimal key-based data sanitizationenhanced garter snake optimization algorithm |
| spellingShingle | Jia Jia Sathiya Sekar Kumarasamy Kiran Sree Pokkuluri K. Suresh Kumar Thella Preethi Priyanka Feng Wang A Robust Authentication and Trust Detection With Privacy Preservation of Data for Fog Computing in VANET Using Adaptive Deep Neural Network IEEE Access Vehicular ad-hoc networks node authentication trust detection adaptive deep neural networks optimal key-based data sanitization enhanced garter snake optimization algorithm |
| title | A Robust Authentication and Trust Detection With Privacy Preservation of Data for Fog Computing in VANET Using Adaptive Deep Neural Network |
| title_full | A Robust Authentication and Trust Detection With Privacy Preservation of Data for Fog Computing in VANET Using Adaptive Deep Neural Network |
| title_fullStr | A Robust Authentication and Trust Detection With Privacy Preservation of Data for Fog Computing in VANET Using Adaptive Deep Neural Network |
| title_full_unstemmed | A Robust Authentication and Trust Detection With Privacy Preservation of Data for Fog Computing in VANET Using Adaptive Deep Neural Network |
| title_short | A Robust Authentication and Trust Detection With Privacy Preservation of Data for Fog Computing in VANET Using Adaptive Deep Neural Network |
| title_sort | robust authentication and trust detection with privacy preservation of data for fog computing in vanet using adaptive deep neural network |
| topic | Vehicular ad-hoc networks node authentication trust detection adaptive deep neural networks optimal key-based data sanitization enhanced garter snake optimization algorithm |
| url | https://ieeexplore.ieee.org/document/10736612/ |
| work_keys_str_mv | AT jiajia arobustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork AT sathiyasekarkumarasamy arobustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork AT kiransreepokkuluri arobustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork AT ksureshkumar arobustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork AT thellapreethipriyanka arobustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork AT fengwang arobustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork AT jiajia robustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork AT sathiyasekarkumarasamy robustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork AT kiransreepokkuluri robustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork AT ksureshkumar robustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork AT thellapreethipriyanka robustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork AT fengwang robustauthenticationandtrustdetectionwithprivacypreservationofdataforfogcomputinginvanetusingadaptivedeepneuralnetwork |