TMEC: A Trust Management Based on Evidence Combination on Attack-Resistant and Collaborative Internet of Vehicles

Abstract

Message transmission in vehicular networks is increasing in popularity which exploits the network nodes to transmit messages using cooperative communication in a multi-hop fashion. But the increasing number of malicious nodes in the high-speed Internet of Vehicles demands additional methodologies to quickly detect the presence of such nodes to avoid serious security consequences. Early detection of malicious nodes, and accurate assessment of complex data to assess the node reliability are of absolute importance in vehicular networks. To this end, this paper proposes a security scheme that uses evidence combination method to combine local data with external evidence to evaluate the reliability of multi-dimensional data received from other peer nodes. In addition, this paper uses European Telecommunications Standards Institute standard and Decentralized Environmental Notification Message, and proposes a trust calculation method based on collaborative filtering by introducing a small-time interval to detect the changes in the node behaviors. While the former solution helps more accurate computation of the direct trust value, the latter scheme can calculate the indirect trust based on recommendations received from neighbors, ultimately to obtain the global trust value. Finally, more effective traffic data can be obtained to help traffic prediction. Experiments conducted under various network scenarios demonstrate that our proposed scheme outperforms the existing trust models, such as precision or recall and can resist bad-mouth attacks, selective-misbehavior attacks, and time-dependent attacks, especially under larger proportions of malicious nodes. INDEX TERMS Cooperative communication, data processing, Internet of Vehicles, intrusion detection, security management.

NOTRINO: A NOvel Hybrid TRust Management Scheme for INternet-of-Vehicles

“…We compared the efficiency of our trust model with three baseline trust management schemes, i.e., ART [29], Chen [50] and TMEC [51]. We chose these trust models as they are hybrid in nature, i.e., they not only evaluate trust on node, but also relies on data trustworthiness for trust calculations.…”

MARINE: Man-in-the-Middle Attack Resistant Trust Model in Connected Vehicles

“…Further, the efficiency of MARINE is computed against a baseline trust model which evaluates trust on the received information from the vehicles via weighted voting method. We chose this method as a baseline trust model as it has been used widely in various trust management methods, such as [31], [48]- [52]. Fig.…”

“…To quickly revoke the malicious nodes from the network, Chen et al proposed a novel evidence-based trust management scheme which integrates both direct and indirect trust [31]. M Eval establishes direct trust at a local level, while indirect trust is computed using BI to filter out the malicious information received from the neighbouring vehicles.…”

Trust-based recommendation systems in Internet of Things: a systematic literature review

“…In order to attain fundamental objectives of vehicular networks, such as, quick discovery of dishonest behaviours and information reliability, Chen et al [126] developed an evidence based security scheme by employing local direct trust as well as indirect trustworthy recommendation of collaborative filtering. While highly accentuating on short time distinguish period, they took benefits of central IoV cloud, vehicular social relationship, user preference and geographical location to offer personalized application and more confident message propagation.…”