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With the increasing frequency, severity and complexity of recent cyber attacks around the world, network intrusion detection has become mandatory and highly sophisticated task. Achieving high performance in network intrusion detection by applying benchmark machine learning classifiers (including deep learning techniques) has become a major challenge in recent times. One of the biggest challenges is improving the memorization capacity and generalization ability of NIDS (Network Intrusion Detection Systems). In this paper, we propose a highly scalable novel wide & deep transfer learning (TL) based stacked GRU (Gated Recurrent Unit) model to deal with multi-dimensional point data and multi-variate time series regression and classification problems in network intrusion detection. The proposed model has the memorization capacity of linear regression model and the generalization ability of deep GRU model …