Implementation of Efficient Security Algorithm and Performance Improvement Through ODMRP Protocol in VANET Environment

Sharma, Prabhat; Pandey, Sagar; Jain, Swapnil

doi:10.1007/s11277-021-09253-x

Cited by 9 publications

(10 citation statements)

References 28 publications

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“…The calculations are carried out in a prime field of 64 bits with a statistical security parameter of = 40. Most effective preprocessing protocol for 2 parties, LowGear version of Overdrive [17], serves as the foundation for the protocol's offline phase. SPDZ-2 is the foundation for the online version [18].…”

Section: Resultsmentioning

confidence: 99%

“…A method for implementing RFID-based navigation system in VANETs was suggested by author [16]. An integrated system that tracks vehicles using both RFID and GPS was developed by work [17]. The radio frequency is used to exchange information in RFID systems, which are susceptible to disruption.…”

Section: Existing Technique For Vanet Data Transmissionmentioning

confidence: 99%

“…Vehicle 𝜚 1 sends vehicle 𝜚 2 an acknowledgement message after receiving it. A reply message is encoded for the channel's security and is given by equation (17):…”

Section: Reply Messagementioning

confidence: 99%

See 2 more Smart Citations

VANET Security Enhancement in Cloud Navigation with Internet of Things Based Trust Model in Deep Learning Architecture

Gnanajeyaraman

Arul

Miichael

et al. 2023

Preprint

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Many intelligent services are available for developing sensor-based vehicle-to-vehicle communication systems through vehicular ad hoc network (VANET). Although neighbour locating and interconnected vehicle sensor processes have been improved by geographic routing methods. However, reliability and data continuity among data routing are crucial for developing transportation systems due to the high level of mobility and realistic environment.The vehicles' wireless communication is also unrestricted and open, making them more vulnerable to security threats and compromising data for improper uses.This research propose novel technique in security improvement in VANET with vehicle cloud based navigation and trust model using deep learning techniques. the vehicle network navigation is carried out using cloud network integrated with IoT and its data transmission to the base station is analysed. then the navigated vehicle security is enhanced using trust based federated transfer quadratic authentication system. the experimental analysis is carried out based on number of vehicles in network as well as its security enhancement. the parameters analysed are throughput, data transmission rate, latency, network traffic analysis, scalability. the proposed technique attained throughput of 95%, data transmission rate of 67%, latency of 56%, network traffic analysis of 76%, scalability of 75%

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Section: Resultsmentioning

confidence: 99%

Section: Existing Technique For Vanet Data Transmissionmentioning

confidence: 99%

See 1 more Smart Citation

VANET Security Enhancement in Cloud Navigation with Internet of Things Based Trust Model in Deep Learning Architecture

Gnanajeyaraman

Arul

Miichael

et al. 2023

Preprint

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“…A similar graph-based study on anomaly detection was presented in [17], where graphs were used to model web traffic in order to identify potential malicious clients. [18] describes the creation of an attention-based temporal Graph Convolutional Network (GCN) method that makes use of temporal features to locate anomalous graph edges within dynamic graphs.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent analytics algorithms in breach detection systems for securing VANETs and data for smart transportation management

Bhuvana

Hashmi

Adhvaryu

et al. 2023

Preprint

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In-vehicle communication has developed into a crucial element of today's driving environment as a result of the expanding additions of sensor-centric communication as well as computing devices inside a vehicle for a variety of purposes, consists of vehicle monitoring, physical wiring minimization as well as driving efficiency. The relevant literature on cyber security for in-vehicle communication methods does not, however, currently offer any certain solutions for in-vehicle cyber hazards. The existing solutions, which mostly rely on protocol-specific security approaches, do not provide a comprehensive security framework for in-vehicle communication. This study aims to develop an effective data transmission and intelligent machine learning technique for smart vehicle management in VANET breach detection. In this study, ensemble adversarial Boltzmann CNN architecture is used to detect breaches. The secure short hop opportunistic local routing protocol is then used to send the data. Throughput, QoS, training accuracy, validation accuracy, and network security analysis are all part of the experimental analysis for a variety of security-based datasets. the proposed technique attainedthroughput of 88%, QoS of 77%, training accuracy of 93%, validation accuracy of 96%, network security analysis of 63%, scalability of 75%.

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“…Sharma et al. successfully implemented a highly effective security algorithm in VANET [22]. Their research also focuses on enhancing performance by integrating the On‐Demand Multicast Routing Protocol (ODMRP).…”

Section: Background and Literature Surveymentioning

confidence: 99%

Adaptive neuro‐fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs

Thiruppathy Kesavan,

Murugavalli,

Premkumar

et al. 2023

IET Communications

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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.

show abstract

Implementation of Efficient Security Algorithm and Performance Improvement Through ODMRP Protocol in VANET Environment

Cited by 9 publications

References 28 publications

VANET Security Enhancement in Cloud Navigation with Internet of Things Based Trust Model in Deep Learning Architecture

VANET Security Enhancement in Cloud Navigation with Internet of Things Based Trust Model in Deep Learning Architecture

Intelligent analytics algorithms in breach detection systems for securing VANETs and data for smart transportation management

Adaptive neuro‐fuzzy inference system and particle swarm optimization: A modern paradigm for securing VANETs

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