Machine Learning Based Trust Model for Misbehaviour Detection in Internet-of-Vehicles

Siddiqui, Sarah; Mahmood, Adnan; Zhang, Wei Emma; Sheng, Quan Z.

doi:10.1007/978-3-030-36808-1_56

Cited by 22 publications

(25 citation statements)

References 8 publications

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“…Machine Learning [111][112] [113] -Machine learning-driven aggregation techniques are normally required two-step process for prediction; i) Unsupervised learning (Clustering) when the training data is not labelled, ii) Multi-class supervised learning (classification) to classify the interactions/nodes into different classes (i.e., trustworthy and/or untrustworthy).…”

Section: A Trust Computationmentioning

confidence: 99%

Understanding the Trustworthiness Management in the Social Internet of Things: A Survey

Sagar¹,

Mahmood²,

Sheng³

et al. 2022

Preprint

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Section: A Trust Computationmentioning

confidence: 99%

Understanding the Trustworthiness Management in the Social Internet of Things: A Survey

Sagar¹,

Mahmood²,

Sheng³

et al. 2022

Preprint

Self Cite

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“…In [24], authors expose a trust model that used SVM to aggregate trust features and compute trust among entities. In [25], researchers utilized ML techniques instead of traditional methods to classify vehicles into trustworthy and untrustworthy. They use real IoT data set to perform ML classifiers precisely SVM and KNN.…”

Section: Comparative Studymentioning

confidence: 99%

Design of a Smart MOOC Trust Model: Towards a Dynamic Peer Recommendation to Foster Collaboration and Learner’s Engagement

Elghomary

Bouzidi

Ajhoun³

2022

Int. J. Emerg. Technol. Learn.

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Recent evolutions in the Internet of Things (IoT) and Social IoT (SIoT) are facilitating collaboration as well as social interactions between entities in various environments, especially Smart Learning Ecosystems (SLEs). However, in these contexts, trust issues become more intense, learners feel suspicious and avoid collaborating with their peers, leading to their demotivation and disengagement. Hence, a trust management system (TMS) has become a crucial challenge to promote qualified collaboration and stimulate learners' engagement. In the literature, several trust models were proposed in various domains, but rarely those that address trust issues in SLEs, especially in MOOCs. While these models exclusively rank the best nodes and fail to detect the untrustworthy ones. Therefore, in this paper, we propose Machine Learning-based trust evaluation model that considers social and dynamic trust parameters to quantify entities' behaviors. It can distinguish trustworthy and untrustworthy behaviors in MOOCs to recommend benign peers while blocking malicious ones to build a dynamic trust-based peer recommendation in the future phase. Our model prevents learners from wasting their time in unprofitable interactions, protects them from malicious actions, and boosts their engagement. A simulation experiment using real-world SIoT datasets and encouraging results show the performance of our trust model.

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“…Deep learning, a subgroup of machine learning, focuses on simulating the way a human brain works to learn from experience (i.e., large volume of data) by employing neural networks with multiple layers [89]. This subsection provides a detailed review of the recent research in trust management models applying the notion of machine learning and deep learning [90][91][92][93][94]. Tangade et al [90] proposed a trust management model that utilizes the notion of deep learning to enhance the reliability and offers reduced latency.…”

Section: Deep/machine Learning-based Trust Modelmentioning

confidence: 99%

“…Siddiqui et al [92] presented a trust management model relying on machine learning to compute an optimal threshold and to identify malicious vehicles in a vehicular network utilizing three contributing parameters, i.e., similarity, familiarity, and packet delivery ratio. The proposed model employs multiple unsupervised learning algorithms to cluster the data for label assignment prior to applying diverse supervised learning algorithms to classify honest and dishonest vehicles, and to acquire an optimal threshold.…”

Section: Deep/machine Learning-based Trust Modelmentioning

confidence: 99%

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A Survey of Trust Management in the Internet of Vehicles

et al. 2021

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Over the past decade, the groundbreaking technological advancements in the Internet of Vehicles (IoV) coupled with the notion of trust have attracted increasing attention from researchers and experts in intelligent transportation systems (ITS), wherein vehicles establish a belief towards their peers in the pursuit of ensuring safe and efficacious traffic flows. Diverse domains have been taking advantage of trust management models in the quest of alleviating diverse insider attacks, wherein messages generated by legitimate users are altered or counterfeited by malicious entities, subsequently, endangering the lives of drivers, passengers, and vulnerable pedestrians. In the course of vehicles forming perceptions towards other participating vehicles, a range of contributing parameters regarding the interactions among these vehicles are accumulated to establish a final opinion towards a target vehicle. The significance of these contributing parameters is typically represented by associating a weighting factor to each contributing attribute. The values assigned to these weighting factors are often set manually, i.e., these values are predefined and do not take into consideration any affecting parameters. Furthermore, a threshold is specified manually that classifies the vehicles into honest and dishonest vehicles relying on the computed trust. Moreover, adversary models as an extension to trust management models in order to tackle the variants of insider attacks are being extensively emphasized in the literature. This paper, therefore, reviews the state of the art in the vehicular trust management focusing on the aforementioned factors such as quantification of weights, quantification of threshold, misbehavior detection, etc. Moreover, an overarching IoV architecture, constituents within the notion of trust, and attacks relating to the IoV have also been presented in addition to open research challenges in the subject domain.

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Machine Learning Based Trust Model for Misbehaviour Detection in Internet-of-Vehicles

Cited by 22 publications

References 8 publications

Understanding the Trustworthiness Management in the Social Internet of Things: A Survey

Understanding the Trustworthiness Management in the Social Internet of Things: A Survey

Design of a Smart MOOC Trust Model: Towards a Dynamic Peer Recommendation to Foster Collaboration and Learner’s Engagement

A Survey of Trust Management in the Internet of Vehicles

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