Abstract:
This paper proposes a deep neural network (DNN) approach for
detecting fake profiles in social networks. The DNN model is trained on a
large dataset of real and fake profiles and is designed to learn complex features
and patterns that distinguish between the two types of profiles. In addition, the
present research aims to determine the minimum set of profile data required
for recognizing fake profiles on Facebook and propose the deep convolutional
neural network method for fake accounts detection on social networks, which
has been developed using 16 features based on content-based and profilebased features. The results demonstrated that the proposed method could
detect fake profiles with an accuracy of 99.4%, equivalent to the achieved
findings based on bigger data sets and more extensive profile information. The
results were obtained with the minimum available profile data. In addition,
in comparison with the other methods that use the same amount and kind
of data, the proposed deep neural network gives an increase in accuracy of
roughly 14%. The proposed model outperforms existing methods, achieving
high accuracy and F1 score in identifying fake profiles. The associated findings
indicate that the proposed model attained an average accuracy of 99% while
considering two distinct scenarios: one with a single theme and another
with a miscellaneous one. The results demonstrate the potential of DNNs
in addressing the challenging problem of detecting fake profiles, which has
significant implications for maintaining the authenticity and trustworthiness
of online social networks.
