scholarly journals A Trust Model Using Edge Nodes and a Cuckoo Filter for Securing VANET under the NLoS Condition

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 609
Author(s):  
Seyed Ahmad Soleymani ◽  
Shidrokh Goudarzi ◽  
Mohammad Hossein Anisi ◽  
Nazri Kama ◽  
Saiful Adli Ismail ◽  
...  
Keyword(s):  
Secure Communication ◽  
Trust Management ◽  
Vehicular Ad Hoc Networks ◽  
Nearest Neighbor ◽  
Negative Impact ◽  
Trust Model ◽  
Line Of Sight ◽  
Communication Overhead ◽  
K Nearest Neighbor ◽  
Cuckoo Filter

Trust, as a key element of security, has a vital role in securing vehicular ad-hoc networks (VANETs). Malicious and selfish nodes by generating inaccurate information, have undesirable impacts on the trustworthiness of the VANET environment. Obstacles also have a negative impact on data trustworthiness by restricting direct communication between nodes. In this study, a trust model based on plausibility, experience, and type of vehicle is presented to cope with inaccurate, incomplete and uncertainty data under both line of sight (LoS) and none-line of sight (NLoS) conditions. In addition, a model using the k-nearest neighbor (kNN) classification algorithm based on feature similarity and symmetry is developed to detect the NLoS condition. Radio signal strength indicator (RSSI), packet reception rate (PDR) and the distance between two vehicle nodes are the features used in the proposed kNN algorithm. Moreover, due to the big data generated in VANET, secure communication between vehicle and edge node is designed using the Cuckoo filter. All obtained results are validated through well-known evaluation measures such as precision, recall, overall accuracy, and communication overhead. The results indicate that the proposed trust model has a better performance as compared to the attack-resistant trust management (ART) scheme and weighted voting (WV) approach. Additionally, the proposed trust model outperforms both ART and WV approaches under different patterns of attack such as a simple attack, opinion tampering attack, and cunning attack. Monte-Carlo simulation results also prove validity of the proposed trust model.

Toward a Security Scheme for an Intelligent Transport System

2019 ◽  
pp. 221-236
Author(s):  
Amira Kchaou ◽  
Ryma Abassi ◽  
Sihem Guemara El Fatmi
Keyword(s):  
Ad Hoc Networks ◽  
Transport System ◽  
Secure Communication ◽  
Trust Management ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Security Scheme ◽  
Hoc Networks

Vehicular ad-hoc networks (VANETs) allow communication among vehicles using some fixed equipment on roads called roads side units. Vehicular communications are used for sharing different kinds of information between vehicles and RSUs in order to improve road safety and provide travelers comfort using exchanged messages. However, falsified or modified messages can be transmitted that affect the performance of the whole network and cause bad situations in roads. To mitigate this problem, trust management can be used in VANET and can be distributive for ensuring safe and secure communication between vehicles. Trust is a security concept that has attracted the interest of many researchers and used to build confident relations among vehicles. Hence, the authors propose a secured clustering mechanism for messages exchange in VANET in order to organize vehicles into clusters based on vehicles velocity, then CH computes the credibility of message using the reputation of vehicles and the miner controls the vehicle's behavior for verifying the correctness of the message.

scholarly journals LSOT: A Lightweight Self-Organized Trust Model in VANETs

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Zhiquan Liu ◽  
Jianfeng Ma ◽  
Zhongyuan Jiang ◽  
Hui Zhu ◽  
Yinbin Miao
Keyword(s):  
Automobile Industry ◽  
Trust Management ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Trust Model ◽  
Trust Evaluation ◽  
Trust Network ◽  
Collusion Attack ◽  
Self Organized ◽  
Hoc Networks

With the advances in automobile industry and wireless communication technology, Vehicular Ad hoc Networks (VANETs) have attracted the attention of a large number of researchers. Trust management plays an important role in VANETs. However, it is still at the preliminary stage and the existing trust models cannot entirely conform to the characteristics of VANETs. This work proposes a novel Lightweight Self-Organized Trust (LSOT) model which contains trust certificate-based and recommendation-based trust evaluations. Both the supernodes and trusted third parties are not needed in our model. In addition, we comprehensively consider three factor weights to ease the collusion attack in trust certificate-based trust evaluation, and we utilize the testing interaction method to build and maintain the trust network and propose a maximum local trust (MLT) algorithm to identify trustworthy recommenders in recommendation-based trust evaluation. Furthermore, a fully distributed VANET scenario is deployed based on the famous Advogato dataset and a series of simulations and analysis are conducted. The results illustrate that our LSOT model significantly outperforms the excellent experience-based trust (EBT) and Lightweight Cross-domain Trust (LCT) models in terms of evaluation performance and robustness against the collusion attack.

scholarly journals Named Data Networking-Based On-Demand Secure Vehicle-To-Vehicle Communications

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qudsia Saleem ◽  
Ikram Ud Din ◽  
Ahmad Almogren ◽  
Ibrahim Alkhalifa ◽  
Hasan Ali Khattak ◽  
...  
Keyword(s):  
Trust Management ◽  
Vehicular Ad Hoc Networks ◽  
Trust Model ◽  
Delivery Ratio ◽  
Named Data Networking ◽  
Hop Count ◽  
On Demand ◽  
Vehicle To Vehicle ◽  
V2v Communications ◽  
Data Networking

The detection of secure vehicles for content placement in vehicle to vehicle (V2V) communications makes a challenging situation for a well-organized dynamic nature of vehicular ad hoc networks (VANET). With the increase in the demand of efficient and adoptable content delivery, information-centric networking (ICN) can be a promising solution for the future needs of the network. ICN provides a direct retrieval of content through its unique name, which is independent of locations. It also performs better in content retrieval with its in-network caching and named-based routing capabilities. Since vehicles are mobile devices, it is very crucial to select a caching node, which is secure and reliable. The security of data is quite important in the vehicular named data networking (VNDN) environment due to its vital importance in saving the life of drivers and pedestrians. To overcome the issue of security and reduce network load in addition to detect a malicious activity, we define a blockchain-based distributive trust model to achieve security, trust, and privacy of the communicating entities in VNDN, named secure vehicle communication caching (SVC-caching) mechanism for the placement of on-demand data. The proposed trust management mechanism is decentralized in nature, which is used to select a trustworthy node for cluster-based V2V communications in the VNDN environment. The SVC-caching strategy is simulated in the NS-2 simulator. The results are evaluated based on one-hop count, delivery ratio, cache hit ratio, and malicious node detection. The results demonstrate that the proposed technique improves the performance based on the selected parameters.

An optimized deep learning-based trust mechanism In VANET for selfish node detection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jyothi N. ◽  
Rekha Patil
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Computational Cost ◽  
Trust Model ◽  
Security And Privacy ◽  
Line Of Sight ◽  
Communication Overhead ◽  
Trust Mechanism ◽  
Content Type ◽  
Selfish Node

Purpose This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection. Design/methodology/approach The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance. Findings A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead. Practical implications The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks. Originality/value This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

scholarly journals Trust Management in Vehicular Ad-Hoc Networks and Internet-of-Vehicles

2020 ◽  
pp. 135-165 ◽  
Author(s):  
Farhan Ahmad ◽  
Asma Adnane ◽  
Chaker Abdelaziz Kerrache ◽  
Virginia N. L. Franqueira ◽  
Fatih Kurugollu
Keyword(s):  
Collision Avoidance ◽  
Trust Management ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Trust Model ◽  
Research Directions ◽  
Internet Of Vehicles ◽  
Current Trends ◽  
Trust Models ◽  
Hoc Networks

Vehicular ad-hoc network (VANET) and internet-of-vehicles (IoV) are complex networks which provide a unique platform for vehicles to communicate and exchange critical information (such as collision avoidance warnings) with each other in an intelligent manner. Thus, the information disseminated in the network should be authentic and originated from legitimate vehicles. Creating a trusted environment in the network can enable the vehicles to identify and revoke malicious ones. Trust is an important concept in VANET and IoV to achieve security in the network, where every vehicle equipped with an appropriate trust model can evaluate the trustworthiness of the received information and its sender. This chapter discusses trust in both VANET and IoV and identifies various trust models developed for VANET and IoV. The contribution of this chapter is threefold. First, the authors present a detailed taxonomy of trust models in VANET and IoV. Second, they provide current trends in the domain of trust management specifically for VANET and IoV, and finally, they provide various open research directions.

Toward a Security Scheme for an Intelligent Transport System

2021 ◽  
pp. 884-896
Author(s):  
Amira Kchaou ◽  
Ryma Abassi ◽  
Sihem Guemara El Fatmi
Keyword(s):  
Ad Hoc Networks ◽  
Transport System ◽  
Secure Communication ◽  
Trust Management ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Security Scheme ◽  
Hoc Networks

Vehicular ad-hoc networks (VANETs) allow communication among vehicles using some fixed equipment on roads called roads side units. Vehicular communications are used for sharing different kinds of information between vehicles and RSUs in order to improve road safety and provide travelers comfort using exchanged messages. However, falsified or modified messages can be transmitted that affect the performance of the whole network and cause bad situations in roads. To mitigate this problem, trust management can be used in VANET and can be distributive for ensuring safe and secure communication between vehicles. Trust is a security concept that has attracted the interest of many researchers and used to build confident relations among vehicles. Hence, the authors propose a secured clustering mechanism for messages exchange in VANET in order to organize vehicles into clusters based on vehicles velocity, then CH computes the credibility of message using the reputation of vehicles and the miner controls the vehicle's behavior for verifying the correctness of the message.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya
Keyword(s):  
Nearest Neighbor ◽  
Information Gain ◽  
Birth Certificate ◽  
Population Data ◽  
Community Services ◽  
Birth Certificates ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Civil Registration ◽  
The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

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