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Network Intrusion Detection plays a very vital role in shielding a Network.However, there are apprehensions regarding the practicality and sustainability of current approaches when looked with the needs of modern networks. Elaborating it more, these concerns relate to the increasing levels of required human interaction in understanding and manipulating the datasetsthus resulting in decreasing levels of detection accuracy. In this paper we have tried to present various techniquesfor intrusion detection, which addresses these concerns. We have detailedvarious Deep Learning techniques with their evaluation using the benchmark KDD Cup ’99 and NSL-KDD andUNSW-NB15 and CICIDS-2017 datasets. Promising results have been obtained from variousmodel, givingimprovements over existing approaches and the strong potential for use in modern NIDS. In this paper endeavours have been made to study Deep Learning model which enable NIDS operation within Modern Networks
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