Adaptive Q‐Learning Trust Management Algorithm for Malicious Node Detection in VANETs
TL;DR
A new trust‐based model for trust management in VANETs is introduced, which is a fully decentralized, modified Q‐Learning approach, and enables dynamic assessments of trust for vehicles (nodes), increases overall network security and trust, and increases the reliability of the data in the network.
Abstract
The distinctive features of dynamic topologies, sparse contacts and evolving adversaries, necessitate that trust models for Vehicles Ad‐hoc Networks (VANETs) be adaptable. We introduced: a new trust‐based model for trust management in VANETs, which is a fully decentralized, modified Q‐Learning approach, and enables dynamic assessments of trust for vehicles (nodes). The system has three primary components: (1) a Q‐Learning Trust Calculation module which assesses trust in a vehicle based on previous interactions, (2) a Malicious Node Detection (MND) module which detects adversarial nodes through the use of adaptive thresholds, and (3) a Malicious Node Removal (MNR) module which removes adversarial nodes from the network through a process known as collaborative revocation. The combined contributions of these components is that the trust model increases overall network security and trust, and increases the reliability of the data in the network by providing a mechanism that is adaptable and resilient to the majority of attacks encountered in vehicular networks.