RETRACTED ARTICLE: Design and development of a hybrid (SDN + SOM) approach for enhancing security in VANET

Abdulkadhim, Fahad Ghalib; Zhang, Yi; Tang, Chengkai; Onaizah, Ameer N.; Ahmed, Badreldeen

doi:10.1007/s13204-021-01908-2

Cited by 18 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Network nodes (RSU and OBU) cannot accommodate the massive amount of data obtained. Distributed denial of service (DDoS) is a changed variant of DoS attacks [63]. It is an attack disseminated by the primary attacker, the "attack operator" of other agents who may be victims inadvertent.…”

Section: Location Privacy Attack Methods and Prevention For Iot In Vanetmentioning

confidence: 99%

A Survey on Location Privacy Attacks and Prevention Deployed with IoT in Vehicular Networks

Ahmed

Deng

Memon

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Vehicular ad hoc networks (VANETs) connect two or more vehicles wirelessly to enable data exchange in an Internet of Things (IoT) environment. In VANETs, location privacy is the most crucial piece of information, and its protection is the top priority. However, the location privacy threats have not been adequately addressed in positioning for IoT in VANETs. This paper provides an overview of location privacy attacks and their solutions to address the problems caused by attacks in any IoT environment. Secondly, we have analyzed specific solutions based on anonymity (pseudonym) and cryptographic solutions using a digital signature technique. This enables to improve user privacy and security of location-based services for IoT in VANET. Moreover, we have proposed a faster 5G solution for the VANETs as it rapidly disseminates the data in fast-moving vehicles.

show abstract

Section: Location Privacy Attack Methods and Prevention For Iot In Vanetmentioning

confidence: 99%

A Survey on Location Privacy Attacks and Prevention Deployed with IoT in Vehicular Networks

Ahmed

Deng

Memon

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Abdulkadhim et al. propose a novel hybrid methodology that combines Software‐Defined Networking (SDN) with Self‐Organizing Maps (SOM) to augment security measures in VANET [13]. The proposed approach integrates SDN and SOM to enhance detecting and preventing anomalies within VANET settings.…”

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

View full text Add to dashboard Cite

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

“…4 SDN is a framework designed to build a flexible network and is easier to manage. 5 It consolidates the control by decoupling the regulator function from the advancing function in the different systems of a network. 6 VANET's network topology is tremendously changed due to the extreme speed of the vehicles.…”

Section: Intoductionmentioning

confidence: 99%

“…Using various techniques, attackers corrupt, update, or delete data or information 4 . SDN is a framework designed to build a flexible network and is easier to manage 5 . It consolidates the control by decoupling the regulator function from the advancing function in the different systems of a network 6 .…”

Section: Intoductionmentioning

confidence: 99%

Residual based temporal attention convolutional neural network for detection of distributed denial of service attacks in software defined network integrated vehicular adhoc network

Karthik,

Lakshmi,

Abraham

et al. 2023

Int J Network Mgmt

View full text Add to dashboard Cite

Software defined network (SDN) integrated vehicular ad hoc network (VANET) is a magnificent technique for smart transportation as it raises the efficiency, safety, manageability, and comfort of traffic. SDN‐integrated VANET (SDN‐int‐VANET) has numerous benefits, but it is susceptible to threats like distributed denial of service (DDoS). Several methods were suggested for DDoS attack detection (AD), but the existing approaches to optimization have given a base for enhancing the parameters. An incorrect selection of parameters results in a poor performance and poor fit to the data. To overcome these issues, residual‐based temporal attention red fox‐convolutional neural network (RTARF‐CNN) for detecting DDoS attacks in SDN‐int‐VANET is introduced in this manuscript. The input data is taken from the SDN DDoS attack dataset. For restoring redundancy and missing value, developed random forest and local least squares (DRFLLS) are applied. Then the important features are selected from the pre‐processed data with the help of stacked contractive autoencoders (St‐CAE), which reduces the processing time of the introduced method. The selected features are classified by residual‐based temporal attention‐convolutional neural network (RTA‐CNN). The weight parameter of RTA‐CNN is optimized with the help of red fox optimization (RFO) for better classification. The introduced method is implemented in the PYTHON platform. The RTARF‐CNN attains 99.8% accuracy, 99.5% sensitivity, 99.80% precision, and 99.8% specificity. The effectiveness of the introduced technique is compared with the existing approaches.

show abstract

RETRACTED ARTICLE: Design and development of a hybrid (SDN + SOM) approach for enhancing security in VANET

Cited by 18 publications

References 26 publications

A Survey on Location Privacy Attacks and Prevention Deployed with IoT in Vehicular Networks

A Survey on Location Privacy Attacks and Prevention Deployed with IoT in Vehicular Networks

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

Residual based temporal attention convolutional neural network for detection of distributed denial of service attacks in software defined network integrated vehicular adhoc network

Contact Info

Product

Resources

About