Trust-based adversary detection in edge computing assisted vehicular networks

Abstract

Article that has been accepted for inclusion in a future issue of a journal. Content is final as presented, with the exception of pagination.

Exploring Trust Modeling and Management Techniques in the Context of Distributed Wireless Networks: A Literature Review

“…First a bipartite graph representation of the network is computed, and based on the edge similarity, which ultimately expresses the trust relationship, the similarity coefficient is computed to eliminate malicious nodes. Tackling the problem of intrusion detection there are also the works of Teng et al [57] and Abhishek and Lim [58]. While Teng et al [57] considered a Bayesian trust model for direct trust, with a fuzzy-based indirect trust approach.…”

“…A trust path evaluation methodology was considered to detect BH attacks. On the other hand, Abhishek and Lim [58] proposed a sufficient statistical-based model for trust computation, with an aggregation approach established on weighted sums to fuse trust values. Additionally, a Gaussian kernel-based similarity metric [82] was considered to differentiate malicious nodes from benign ones.…”

RSU-Based Online Intrusion Detection and Mitigation for VANET

“…Trust-centric models are based on voting or scoring schemes in which the reliability of a node broadcasting a message is voted on by the other nodes receiving the message. Once the cumulative voting score exceeds a level against the node, it is declared as an intruder, and its message dissemination is blocked [ 17 , 18 , 19 ]. A data-centric approach evaluates the driver’s behavior with respect to shared messages.…”