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Network intrusion is a huge harmful activity to the privacy of the data sharing network. The activity will result in a cyber-attack, which causes damage to the system as well as the user’s data. Unauthorized activities such as data tampering, illegal access to data and theft of credentials are increasing on the internet world every day. The detection of intrusion may be done by multiple methodologies; still, it is the biggest issue in the networks. Hence, an automated attack classification model is required to promote classification accuracy with fewer error possibilities based on the input parameters. To get relief from the insecurity of data, this paper presents an innovative model using deep networks. The proposed model is a deep learning based network intrusion detection system using a chaotic optimization strategy. The method is pre-processed using data cleansing and M-squared normalization. After pre-processing, the …