# Trust – IoV: An open benchmark dataset for trust management in the internet of vehicles

## TL;DR

This paper introduces a new benchmark dataset for trust management in the Internet of Vehicles, designed to detect and study trust-based attacks.

## Contribution

The paper presents the first publicly available trust-based IoV dataset for detecting trust-based attacks.

## Key findings

- The dataset includes 96 vehicles, some of which are intelligent malicious ones capable of launching trust-based attacks.
- A total of 1,048,576 interactions were recorded, with trust quantified using four key parameters.
- The dataset provides a foundation for future research on trust management in IoV networks.

## Abstract

Whilst advancements in information and communication technologies and artificial intelligence have considerably enhanced the capabilities of the Internet of Vehicles (IoV) paradigm, ensuring the security of this highly dynamic and decentralized network still remains a critical challenge. Accordingly, the notion of trust has been lately employed by researchers in academia and industry with an intent to primarily tackle the internal attacks that have the potential to considerably jeopardize the security of an IoV network. However, as of date, there is a lack of publicly released trust-based IoV datasets particularly designed for detecting trust-based attacks. Therefore, this paper envisages a novel trust-based IoV dataset encompassing 96 vehicles, i.e., both trustworthy and intelligent malicious, with the latter one equipped with the ability to instigate trust-based attacks (on-off attacks, self-promoting attacks, and opportunistic attacks). A total of 1,048,576 interactions have been carried out by the said vehicles at different time instances. Four salient trust parameters, i.e., experience of interaction, frequency of interaction, timeliness of interaction, and quality of received messages, have been employed to quantify trust of each interaction segment of the vehicles. As the first trust-based IoV dataset encompassing information pertinent to the trust-based attacks, it provides a robust basis for advancing future research in this specialized domain.