CMP-BLOCKCHAIN BASED EVENT DETECTION & TRUST VERIFICATION USING NATURAL LANGUAGE PROCESSING AND ML

Abstract

Social Networks represent a cornerstone
of our daily life, where the so-called
social reviewing systems (SRSs) play a
key role in our daily lives and are used
to access data typically in the form of
reviews. Due to their importance, social
networks must be trustworthy and
secure, so that their shared information
can be used by the people without any
concerns, and must be protected against
possible attacks and misuses. One of the
most critical attacks against the
reputation system is represented by
mendacious reviews. As this kind of
attacks can be conducted by legitimate
users of the network, a particularly
powerful solution is to exploit trust
management, by assigning a trust degree
to users, so that people can weigh the
gathered data based on such trust
degrees. Trust management within the
context of SRSs is particularly
challenging, as determining incorrect
behaviors is subjective and hard to be
fully automatized. Several attempts in
the current literature have been
proposed; however, such an issue is still
far from been completely resolved. In
this study, we propose a solution against
mendacious reviews that combines
fuzzy logic and the theory of evidence
by modeling trust management as a
multicriteria multiexpert decision
making and exploiting the novel concept
of time-dependent and contentdependent crown consensus. We
empirically proved that our approach
outperforms the main related works
approaches, also in dealing with
sockpuppet attacks