Abstract
Nowadays, artificial neural networks have the potential to learn and improve the performance of machines in terms of various aspects. It provides a platform for humans which make life simpler and more flexible. In ANN, machines are trained and learn to work efficiently with high accuracy. There is a wide variety of application fields like healthcare, military, finance, home appliances, etc. By detecting intrusion using an artificial neural network, the security of the system can be enhanced. Motivated by this, we proposed a novel approach that uses recurrent neural networks to classify the type of social media network intrusion according to the given input data. The proposed model is trained and tested on a large dataset resulting in high accuracy. Afterward, the proposed deep learning model is used for detecting intrusion in social media networks, and accordingly, classification is done with the new set of input data.
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Lal, N., Kumar, S., Kaidan, G. (2022). A Deep Learning Approach for Anomalous User-Intrusion Detection in Social Media Network System. In: Hong, TP., Serrano-Estrada, L., Saxena, A., Biswas, A. (eds) Deep Learning for Social Media Data Analytics. Studies in Big Data, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-10869-3_14
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DOI: https://doi.org/10.1007/978-3-031-10869-3_14
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