A New Scheme for Detecting Malicious Nodes in Vehicular Ad Hoc Networks Based on Monitoring Node Behavior

Alkhalidy, Muhsen; Al-Serhan, Atalla Fahed; Alsarhan, Ayoub; Igried, Bashar

doi:10.3390/fi14080223

Cited by 20 publications

(10 citation statements)

References 23 publications

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“…Similarly, Muhsen et al. [ 36 ] proposed a fuzzy logic driven trust evaluation framework that takes into account the neighbor recommendations, direct trust and the message accuracy as parameters to compute trust scores. For recommendations, opinions of both neighboring vehicles and the neighboring roadside units (RSUs) are considered, whereas for direct trust, historical interactions among the particular RSU and the vehicle being evaluated are taken.…”

Section: Related Workmentioning

confidence: 99%

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles

Siddiqui

Mahmood

Sheng

et al. 2023

Sensors

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The rapid proliferation of the emerging yet promising notion of the Internet-of-Vehicles (IoV) has led to the development of a variety of conventional trust assessment schemes to tackle insider attackers. The primary reliance of these frameworks is on the accumulation of individual trust attributes. While aggregating these influential parameters, weights are often associated with each individual attribute to reflect its impact on the final trust score. It is of paramount importance that such weights be precise to lead to an accurate trust assessment. Moreover, the value of the minimum acceptable trust threshold employed for the identification of dishonest vehicles needs to be carefully defined to avoid delayed or erroneous detection. This paper employs an IoT data set from CRAWDAD by suitably transforming it into an IoV format. This data set encompasses information regarding 18,226 interactions among 76 nodes, both honest and dishonest. First, the influencing parameters (i.e., packet delivery ratio, familiarity, timeliness and interaction frequency) were computed, and two feature matrices were formed. The first matrix (FM1) takes into account all the pairwise individual parameters as individual features, whereas the second matrix (FM2) considers the average of all pairwise computations performed for each individual parameter as one feature. Subsequently, unsupervised learning is employed to achieve the ground truth prior to applying supervised machine learning algorithms for classification purposes. It is worth noting that Subspace KNN yielded a perfect precision, recall, and the F1-score equal to 1 for individual parametric scores, whereas Subspace Discriminant returned an ideal precision, recall, and the F1-score equal to 1 for mean parametric scores. It is also evident from extensive simulations that FM2 yielded more accurate classification results compared to FM1. Furthermore, decision boundaries among honest and dishonest vehicles have also been computed for respective feature matrices.

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Section: Related Workmentioning

confidence: 99%

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles

Siddiqui

Mahmood

Sheng

et al. 2023

Sensors

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“…They have thought about the possibility of a false crash alert in [11] and fake vehicle position data in PCN. A novel Misbehavior Based Reputation Management Scheme (MBRMS) for detection as well as filtering of false data in vehicular ad-hoc networks has been proposed by work [12]. It consists of 3 parts: misbehavior detection, event rebroadcast, and global eviction methods.…”

Section: Related Workmentioning

confidence: 99%

Machine learning based novel frameworks developments and architectures for secured communication in VANETs for smart transportation

Chakraborty

Kumar

Awasthi

et al. 2023

Preprint

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Vehicle ad hoc networks have made intelligent transportation systems that significantly increase road safety as well as management possible. Vehicles can now communicate and share information about the road using this new technology. However, malicious users might inject fake emergency alerts into VANET, making it impossible for nodes to access accurate road information. In vehicular ad hoc networks, assessing credibility of nodes has become a crucial task to ensure reliability as well as trustworthiness of data. Using machine learning methods, this study proposes a novel security technique that improves communication and intruder detection in VANET for smart transportation. Ciphertext-policy game theory encryption analysis for smart transportation is used here to improve the security of the VANET. Fuzzy rule-based encoder perceptron neural networks are utilized in the detection of the VANET intruder. For a variety of network datasets, the experimental analysis is conducted in terms of throughput, QoS, latency, computational cost, and data transmission rate.

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“…Alkhalidy et al proposed [115] a new strategy for catching malicious nodes in the vehicle network. Malicious vehicles send wrong emergency information in the network to restrict nodes from accessing the channel to receive road information.…”

Section: Sv (Secured Vanet)mentioning

confidence: 99%

Investigation of Different Mechanisms to Detect Misbehaving Nodes in Vehicle Ad-Hoc Networks (VANETs)

Nobahari,

Bakhshayeshi Avval,

Akhbari

et al. 2023

Security and Communication Networks

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The vehicle ad-hoc network (VANET) is a crucial technology that will play a significant role in shaping the future of transition systems, which is widely used as a subset of ad-hoc networks. VANET aims to ensure driver safety by establishing independent communication with nearby vehicles. A key requirement for successful data transmission is cooperation among nodes, as factors such as high mobility, limited radio range, signal fading, and noise lead to packet loss. Security issues in vehicle ad-hoc networks have recently become a major concern. One factor that affects security is the presence of abusive nodes in the network. Like selfish nodes, they are reluctant to share their sources with their neighbors and try to keep their property. The misbehavior of malicious nodes includes dissemination of false traffic information, false location information, and redirection of packets to a wrong path, retransmission of packets, impersonation, so these nodes should be tracked down to ensure the operation of the network. This article provides a complete summary of various research works proposed to detect selfish and malicious nodes and isolate them from honest vehicles. This review article first describes the types of attacks. It then presents the methods proposed by researchers to deal with uncooperative nodes and compares their performance based on parameters such as the number of misbehaving nodes detected, overhead, throughput, layer involved in the attacks.

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A New Scheme for Detecting Malicious Nodes in Vehicular Ad Hoc Networks Based on Monitoring Node Behavior

Cited by 20 publications

References 23 publications

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles

Machine learning based novel frameworks developments and architectures for secured communication in VANETs for smart transportation

Investigation of Different Mechanisms to Detect Misbehaving Nodes in Vehicle Ad-Hoc Networks (VANETs)

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