Abstract
A deep learning approach has been proposed for the classification of cluster instances as being intrusive or not intrusive. Mini-batch Adam optimizer was used due to a large number of hidden layers in the model. Massive amounts of data accumulated for training prevented the model from overfitting. After extensive testing of data with various algorithms, it was found that deep learning model with Adam optimizer outperformed others.
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Prasad, S., Agyeya, O., Singh, P., Krishnakumar, S.S. (2021). Deep Network for Network Intrusion with Concept Drift. In: Ranganathan, G., Chen, J., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 145. Springer, Singapore. https://doi.org/10.1007/978-981-15-7345-3_79
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DOI: https://doi.org/10.1007/978-981-15-7345-3_79
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