• Zohra Anzar Shaikh
• Vidya Dhamdhere

Network intrusion identification frameworks play a pivotal role in guarding computer networks. As of late, one of the fundamental concentrations within Network Intrusion Detection System(NIDS) inquire about the usage and application of Machine Learning(ML) Techniques. This paper proposed to enable NIDS network traffic for novel deep learning model. The novel approach proposes non-symmetric deep auto encoder(NDAE) for unsupervised feature learning. Moreover, it proposes novel deep learning classification display built utilizing stacked NDAEs. Our proposed classifier has been executed in KDD Cup '99 and NSL-KDD data-sets. The KDD Cup 99 and NSL-KDD dataset particularly are performance evaluated network intrusion detection datasets. 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.

Deep Learning, Machine learning, Intrusion Detection, Auto Encoder, KDD, Network Security, Novel Approach.

"Unsupervised Feature Learning using a Novel Non-Symmetric Deep Auto encoder(NDAE) Model for NIDS Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.1318-1321, June-2020, Available :http://www.jetir.org/papers/JETIR2006191.pdf

"Unsupervised Feature Learning using a Novel Non-Symmetric Deep Auto encoder(NDAE) Model for NIDS Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp1318-1321, June-2020, Available at : http://www.jetir.org/papers/JETIR2006191.pdf