  A trust management system (TMS) is an integral component of any IoT network.
A reliable trust management system must guarantee the network security, data
integrity, and act as a referee that promotes legitimate devices, and punishes
any malicious activities. Trust scores assigned by TMSs reflect devices'
reputations, which can help predict the future behaviours of network entities
and subsequently judge the reliability of different network entities in IoT
networks. Many TMSs have been proposed in the literature, these systems are
designed for small-scale trust attacks, and can deal with attacks where a
malicious device tries to undermine TMS by spreading fake trust reports.
However, these systems are prone to large-scale trust attacks. To address this
problem, in this paper, we propose a TMS for large-scale IoT systems called
Trust2Vec, which can manage trust relationships in large-scale IoT systems and
can mitigate large-scale trust attacks that are performed by hundreds of
malicious devices. Trust2Vec leverages a random-walk network exploration
algorithm that navigates the trust relationship among devices and computes
trust network embeddings, which enables it to analyze the latent network
structure of trust relationships, even if there is no direct trust rating
between two malicious devices. To detect large-scale attacks, suck as
self-promoting and bad-mouthing, we propose a network embeddings community
detection algorithm that detects and blocks communities of malicious nodes. The
effectiveness of Trust2Vec is validated through large-scale IoT network
simulation. The results show that Trust2Vec can achieve up to 94\% mitigation
rate in various network scenarios.
