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Sparse events, such as malign attacks in real-time network traffic, havecaused big organisations an immense hike in revenue loss. This is due to theexcessive growth of the network and its exposure to a plethora of people. Thestandard methods used to detect intrusions are not promising and havesignificant failure to identify new malware. Moreover, the challenges inhandling high volume data with sparsity, high false positives, fewer detectionrates in minor class, training time and feature engineering of thedimensionality of data has promoted deep learning to take over the task withless time and great results. The existing system needs improvement in solvingreal-time network traffic issues along with feature engineering. Our proposedwork overcomes these challenges by giving promising results using deep-stackedautoencoders in two stages. The two-stage deep learning combines with shallowlearning using the random forest for classification in the second stage. Thismade the model get well with the latest Canadian Institute for Cybersecurity -Intrusion Detection System 2017 (CICIDS-2017) dataset. Zero false positiveswith admirable detection accuracy were achieved.

Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System