ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV
A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/3/818 |
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| author | Mahmood ul Hassan Amin A. Al-Awady Abid Ali Sifatullah Muhammad Akram Muhammad Munwar Iqbal Jahangir Khan Yahya Ali Abdelrahman Ali |
| author_facet | Mahmood ul Hassan Amin A. Al-Awady Abid Ali Sifatullah Muhammad Akram Muhammad Munwar Iqbal Jahangir Khan Yahya Ali Abdelrahman Ali |
| author_sort | Mahmood ul Hassan |
| collection | DOAJ |
| description | A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters. |
| first_indexed | 2024-03-08T03:48:37Z |
| format | Article |
| id | doaj.art-37c7f1c76e5c4a409da0557d8542d8de |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:48:37Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-37c7f1c76e5c4a409da0557d8542d8de2024-02-09T15:21:55ZengMDPI AGSensors1424-82202024-01-0124381810.3390/s24030818ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODVMahmood ul Hassan0Amin A. Al-Awady1Abid Ali2Sifatullah3Muhammad Akram4Muhammad Munwar Iqbal5Jahangir Khan6Yahya Ali Abdelrahman Ali7Department of Computer Skills, Deanship of Preparatory Year, Najran University, Najran 66241, Saudi ArabiaDepartment of Computer Skills, Deanship of Preparatory Year, Najran University, Najran 66241, Saudi ArabiaDepartment of Computer Science, University of Engineering and Technology, Taxila 48080, PakistanDepartment of Computer Science, University of Engineering and Technology, Taxila 48080, PakistanDepartment of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 66241, Saudi ArabiaDepartment of Computer Science, University of Engineering and Technology, Taxila 48080, PakistanDepartment of Computer Science, Applied College Mohyail Asir, King Khalid University, Abha 62529, Saudi ArabiaDepartment of Information System, College of Computer Science and Information Systems, Najran University, Najran 66241, Saudi ArabiaA vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.https://www.mdpi.com/1424-8220/24/3/818artificial neural networkclusteringAODVVANETsecure routingblack hole attack |
| spellingShingle | Mahmood ul Hassan Amin A. Al-Awady Abid Ali Sifatullah Muhammad Akram Muhammad Munwar Iqbal Jahangir Khan Yahya Ali Abdelrahman Ali ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV Sensors artificial neural network clustering AODV VANET secure routing black hole attack |
| title | ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV |
| title_full | ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV |
| title_fullStr | ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV |
| title_full_unstemmed | ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV |
| title_short | ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV |
| title_sort | ann based intelligent secure routing protocol in vehicular ad hoc networks vanets using enhanced aodv |
| topic | artificial neural network clustering AODV VANET secure routing black hole attack |
| url | https://www.mdpi.com/1424-8220/24/3/818 |
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