An Efficient Framework for Fake Profile Identification Using Metaheuristic and Deep Learning Techniques

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W. Rose Varuna, K. Shalini, Maria Elna Akkalya Roy


The rise of fraudulent accounts is one of the most severe concerns in the digital age. Fake accounts have been judged particularly destructive to both OSN service providers and their clients, and if not caught early, they might become considerably more troublesome in the future. A user becomes a target for an attacker when he or she creates an OSN account. Fake accounts may trace a user's movements and encourage them to do things they shouldn't. This article gives a comprehensive description of the framework for detecting false profiles in social networks. Account vulnerability assessment is monitored using an open-source big data system for false profile detection. In terms of public and private profile traits, there are ethical issues in data collecting.The LSTM configuration used to implement the proposed framework is also detailed. The suggested method has a training accuracy of 98 percent and a validation accuracy of 97.9%.

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