An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication

The vehicular ad hoc network, VANET, is one of the most popular and promising technologies in intelligent transportation today. However, VANET is susceptible to several vulnerabilities that result in an intrusion. This intrusion must be solved before VANET technology can be adopted. In this study, w...

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Vydáno v:	Mathematics (Basel) Ročník 10; číslo 21; s. 4030
Hlavní autoři:	Marwah, Gagan Preet Kour, Jain, Anuj, Malik, Praveen Kumar, Singh, Manwinder, Tanwar, Sudeep, Safirescu, Calin Ovidiu, Mihaltan, Traian Candin, Sharma, Ravi, Alkhayyat, Ahmed
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.11.2022
Témata:	Ad hoc networks (Computer networks) Algorithms Ant colony optimization Assaults Automobiles Bandwidths Communication Control cuckoo search Delay time Denial of service attacks dragonfly Effectiveness Energy consumption Industry standards Intelligent vehicles Intrusion Machine learning Mobile ad hoc networks Network latency Nodes Optimization Public safety Quality of service architectures Resource management rider optimization algorithm Robust statistics Routing (telecommunications) Search algorithms Shortest-path problems Software Stagnation support vector machine Tolls Traffic congestion Transportation planning VANET Vehicles whale optimization algorithm India
ISSN:	2227-7390, 2227-7390
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Abstract	The vehicular ad hoc network, VANET, is one of the most popular and promising technologies in intelligent transportation today. However, VANET is susceptible to several vulnerabilities that result in an intrusion. This intrusion must be solved before VANET technology can be adopted. In this study, we suggest a unique machine learning technique to improve VANET’s effectiveness. The proposed method incorporates two phases. Phase I detects the DDoS attack using a novel machine learning technique called SVM-HHO, which provides information about the vehicle. Phase II mitigates the impact of a DDoS attack and allocates bandwidth using a reliable resources management technique based on the hybrid whale dragonfly optimization algorithm (H-WDFOA). This proposed model could be an effective technique predicting and utilizing reliable information that provides effective results in smart vehicles. The novel machine learning-based technique was implemented through MATLAB and NS2 platforms. Network quality measurements included congestion, transit, collision, and QoS awareness cost. Based on the constraints, a different cost framework was designed. In addition, data preprocessing of the QoS factor and total routing costs were considered. Rider integrated cuckoo search (RI-CS) is a novel optimization algorithm that combines the concepts of the rider optimization algorithm (ROA) and cuckoo search (CS) to determine the optimal route with the lowest routing cost. The enhanced hybrid ant colony optimization routing protocol (EHACORP) is a networking technology that increases efficiency by utilizing the shortest route. The shortest path of the proposed protocol had the lowest communication overhead and the fewest number of hops between sending and receiving vehicles. The EHACORP involved two stages. To find the distance between cars in phase 1, EHACORP employed a method for calculating distance. Using starting point ant colony optimization, the ants were guided in phase 2 to develop the shortest route with the least number of connections to send information. The relatively short approach increases protocol efficiency in every way. The pairing of DCM and SBACO at H-WDFOA-VANET accelerated packet processing, reduced ant search time, eliminated blind broadcasting, and prevented stagnation issues. The delivery ratio and throughput of the H-WDFOA-packet VANET benefitted from its use of the shortest channel without stagnation, its rapid packet processing, and its rapid convergence speed. In conclusion, the proposed hybrid whale dragonfly optimization approach (H-WDFOA-VANET) was compared with industry standard models, such as rider integrated cuckoo search (RI-CS) and enhanced hybrid ant colony optimization routing protocol (EHACORP). With the proposed method, throughput could be increased. The proposed system had energy consumption values of 2.00000 mJ, latency values of 15.61668 s, and a drop at node 60 of 0.15759. Additionally, a higher throughput was achieved with the new method. With the suggested method, it is possible to meet the energy consumption targets, delay value, and drop value at node 60. The proposed method reduces the drop value at node 80 to 0.15504, delay time to 15.64318 s, and energy consumption to 2.00000 mJ. These outcomes demonstrate the effectiveness of our proposed method. Thus, the proposed system is more efficient than existing systems.
AbstractList	The vehicular ad hoc network, VANET, is one of the most popular and promising technologies in intelligent transportation today. However, VANET is susceptible to several vulnerabilities that result in an intrusion. This intrusion must be solved before VANET technology can be adopted. In this study, we suggest a unique machine learning technique to improve VANET’s effectiveness. The proposed method incorporates two phases. Phase I detects the DDoS attack using a novel machine learning technique called SVM-HHO, which provides information about the vehicle. Phase II mitigates the impact of a DDoS attack and allocates bandwidth using a reliable resources management technique based on the hybrid whale dragonfly optimization algorithm (H-WDFOA). This proposed model could be an effective technique predicting and utilizing reliable information that provides effective results in smart vehicles. The novel machine learning-based technique was implemented through MATLAB and NS2 platforms. Network quality measurements included congestion, transit, collision, and QoS awareness cost. Based on the constraints, a different cost framework was designed. In addition, data preprocessing of the QoS factor and total routing costs were considered. Rider integrated cuckoo search (RI-CS) is a novel optimization algorithm that combines the concepts of the rider optimization algorithm (ROA) and cuckoo search (CS) to determine the optimal route with the lowest routing cost. The enhanced hybrid ant colony optimization routing protocol (EHACORP) is a networking technology that increases efficiency by utilizing the shortest route. The shortest path of the proposed protocol had the lowest communication overhead and the fewest number of hops between sending and receiving vehicles. The EHACORP involved two stages. To find the distance between cars in phase 1, EHACORP employed a method for calculating distance. Using starting point ant colony optimization, the ants were guided in phase 2 to develop the shortest route with the least number of connections to send information. The relatively short approach increases protocol efficiency in every way. The pairing of DCM and SBACO at H-WDFOA-VANET accelerated packet processing, reduced ant search time, eliminated blind broadcasting, and prevented stagnation issues. The delivery ratio and throughput of the H-WDFOA-packet VANET benefitted from its use of the shortest channel without stagnation, its rapid packet processing, and its rapid convergence speed. In conclusion, the proposed hybrid whale dragonfly optimization approach (H-WDFOA-VANET) was compared with industry standard models, such as rider integrated cuckoo search (RI-CS) and enhanced hybrid ant colony optimization routing protocol (EHACORP). With the proposed method, throughput could be increased. The proposed system had energy consumption values of 2.00000 mJ, latency values of 15.61668 s, and a drop at node 60 of 0.15759. Additionally, a higher throughput was achieved with the new method. With the suggested method, it is possible to meet the energy consumption targets, delay value, and drop value at node 60. The proposed method reduces the drop value at node 80 to 0.15504, delay time to 15.64318 s, and energy consumption to 2.00000 mJ. These outcomes demonstrate the effectiveness of our proposed method. Thus, the proposed system is more efficient than existing systems.
Audience	Academic
Author	Marwah, Gagan Preet Kour Tanwar, Sudeep Singh, Manwinder Safirescu, Calin Ovidiu Sharma, Ravi Alkhayyat, Ahmed Jain, Anuj Malik, Praveen Kumar Mihaltan, Traian Candin
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Cites_doi	10.3233/JIFS-169669 10.1109/ACCESS.2020.3021435 10.1007/s12652-021-03176-y 10.1155/2018/2874509 10.1016/j.future.2017.11.043 10.3390/sym13010047 10.1109/ACCESS.2020.2967478 10.1109/TSIPN.2018.2801622 10.1002/dac.4954 10.1155/2018/9804061 10.1007/s00607-021-01001-0 10.1109/MNET.2018.1700460 10.4018/IJSPPC.2020100101 10.1109/ACCESS.2019.2960367 10.1007/s11277-020-07549-y 10.1109/TITS.2019.2904953 10.1109/TVT.2019.2899627 10.1186/s13677-018-0109-4 10.1016/j.eswa.2015.12.006 10.3390/agriculture12060793 10.1109/ACCESS.2021.3120626 10.1109/ACCESS.2020.3009733 10.1109/LCOMM.2017.2766636 10.1109/ACCESS.2021.3072922 10.1109/ACCESS.2019.2948382 10.1038/s41598-022-14255-1 10.1016/j.isatra.2021.07.017 10.1109/ACCESS.2018.2875678 10.1007/978-3-030-31239-8_22 10.1049/iet-com.2020.0477 10.1109/MCOM.2017.1601105 10.1007/s11276-020-02413-0 10.1007/s11036-016-0676-x
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Snippet	The vehicular ad hoc network, VANET, is one of the most popular and promising technologies in intelligent transportation today. However, VANET is susceptible...
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SubjectTerms	Ad hoc networks (Computer networks) Algorithms Ant colony optimization Assaults Automobiles Bandwidths Communication Control cuckoo search Delay time Denial of service attacks dragonfly Effectiveness Energy consumption Industry standards Intelligent vehicles Intrusion Machine learning Mobile ad hoc networks Network latency Nodes Optimization Public safety Quality of service architectures Resource management rider optimization algorithm Robust statistics Routing (telecommunications) Search algorithms Shortest-path problems Software Stagnation support vector machine Tolls Traffic congestion Transportation planning VANET Vehicles whale optimization algorithm
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Title	An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
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