Adaptive neuro‐fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs
Abstract Vehicular Adhoc Networks (VANET) facilitate inter‐vehicle communication using their dedicated connection infrastructure. Numerous advantages and applications exist associated with this technology, with road safety particularly noteworthy. Ensuring the transportation and security of informat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-12-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12692 |
| _version_ | 1797405504675250176 |
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| author | V Thiruppathy Kesavan S Murugavalli Manoharan Premkumar Shitharth Selvarajan |
| author_facet | V Thiruppathy Kesavan S Murugavalli Manoharan Premkumar Shitharth Selvarajan |
| author_sort | V Thiruppathy Kesavan |
| collection | DOAJ |
| description | Abstract Vehicular Adhoc Networks (VANET) facilitate inter‐vehicle communication using their dedicated connection infrastructure. Numerous advantages and applications exist associated with this technology, with road safety particularly noteworthy. Ensuring the transportation and security of information is crucial in the majority of networks, similar to other contexts. The security of VANETs poses a significant challenge due to the presence of various types of attacks that threaten the communication infrastructure of mobile vehicles. This research paper introduces a new security scheme known as the Soft Computing‐based Secure Protocol for VANET Environment (SC‐SPVE) method, which aims to tackle security challenges. The SC‐SPVE technique integrates an adaptive neuro‐fuzzy inference system and particle swarm optimisation to identify different attacks in VANETs efficiently. The proposed SC‐SPVE method yielded the following average outcomes: a throughput of 148.71 kilobits per second, a delay of 23.60 ms, a packet delivery ratio of 95.62%, a precision of 92.80%, an accuracy of 99.55%, a sensitivity of 98.25%, a specificity of 99.65%, and a detection time of 6.76 ms using the Network Simulator NS2. |
| first_indexed | 2024-03-09T03:10:57Z |
| format | Article |
| id | doaj.art-686e9c62a9824cfb99703515b7a199b2 |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-03-09T03:10:57Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-686e9c62a9824cfb99703515b7a199b22023-12-04T03:45:22ZengWileyIET Communications1751-86281751-86362023-12-0117192219223610.1049/cmu2.12692Adaptive neuro‐fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETsV Thiruppathy Kesavan0S Murugavalli1Manoharan Premkumar2Shitharth Selvarajan3Department of Information Technology Dhanalakshmi Srinivasan Engineering College Perambalur Tamilnadu IndiaDepartment of Artificial Intelligence K.Ramakrishnan College of Technology Trichy Tamilnadu IndiaDepartment of Electrical and Electronics Engineering Dayananda Sagar College of Engineering Bengaluru Karnataka IndiaDepartment of Computer Science Kebri Dehar University Kebri Dehar EthiopiaAbstract Vehicular Adhoc Networks (VANET) facilitate inter‐vehicle communication using their dedicated connection infrastructure. Numerous advantages and applications exist associated with this technology, with road safety particularly noteworthy. Ensuring the transportation and security of information is crucial in the majority of networks, similar to other contexts. The security of VANETs poses a significant challenge due to the presence of various types of attacks that threaten the communication infrastructure of mobile vehicles. This research paper introduces a new security scheme known as the Soft Computing‐based Secure Protocol for VANET Environment (SC‐SPVE) method, which aims to tackle security challenges. The SC‐SPVE technique integrates an adaptive neuro‐fuzzy inference system and particle swarm optimisation to identify different attacks in VANETs efficiently. The proposed SC‐SPVE method yielded the following average outcomes: a throughput of 148.71 kilobits per second, a delay of 23.60 ms, a packet delivery ratio of 95.62%, a precision of 92.80%, an accuracy of 99.55%, a sensitivity of 98.25%, a specificity of 99.65%, and a detection time of 6.76 ms using the Network Simulator NS2.https://doi.org/10.1049/cmu2.12692intrusion detection systemoptimizationsecuritysoft computingvehicular ad‐hoc networks (VANET) |
| spellingShingle | V Thiruppathy Kesavan S Murugavalli Manoharan Premkumar Shitharth Selvarajan Adaptive neuro‐fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs IET Communications intrusion detection system optimization security soft computing vehicular ad‐hoc networks (VANET) |
| title | Adaptive neuro‐fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs |
| title_full | Adaptive neuro‐fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs |
| title_fullStr | Adaptive neuro‐fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs |
| title_full_unstemmed | Adaptive neuro‐fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs |
| title_short | Adaptive neuro‐fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs |
| title_sort | adaptive neuro fuzzy inference system and particle swarm optimization a modern paradigm for securing vanets |
| topic | intrusion detection system optimization security soft computing vehicular ad‐hoc networks (VANET) |
| url | https://doi.org/10.1049/cmu2.12692 |
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