Deep Neural Network for Detecting Fake Profiles in Social Networks

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.