A SURVEY ON INTRUSION DETECTION SYSTEM USING MACHINE LEARNING FRAMEWORK
Spurti Shinde, Naganath Buvasaheb Khade · Journal of Emerging Technologies and Innovative Research · 2020
Network intrusion detection systems play a crucial role in defending computer networks. In recent years, one of the main focuses within NIDS research has been the application of machine learning techniques. This paper proposes a novel deep learning model to enable NIDS operation within modern networks. The model shows a combination of deep and machine learning, capable of correctly analyzing a wide-range of network traffic. The novel approach proposes non-symmetric deep auto encoder (NDAE) for unsupervised feature learning. Moreover, additionally proposes novel deep learning classification display built utilizing stacked NDAEs. Our proposed classifier has been executed in Graphics processing unit and assessed utilizing the benchmark using KDD Cup '99 and NSL-KDD datasets. The performance evaluated network intrusion detection analysis datasets, particularly KDD Cup 99 and NSL-KDD dataset. The contribution work is to implement intrusion prevention system (IPS) contains IDS functionality but more sophisticated systems which are capable of taking immediate action in order to prevent or reduce the malicious behavior.