• N.K.Barpanda
• Premansu Sekhara Rath

: Every now and then we are very much related to the network. It may be internet or intranet. We generally share personal information as well as organizational information through the network. So we should secure our network. Since last twenty years various NIDS have been developed and widely used in the network which detects efficiently the various network threats. One of the contexts of NIDS is generation of alarms when an attack is detected. But sometimes the NIDS produces false alarms. Many machine learning approaches have been applied to reduce false alarm rate, but the approaches are not multi-viewed based approach. Those approaches use single function to model a particular view and then optimize all the functions in the learning process. But here, we develop MVPSys, a practical approach to reduce false alarm which works efficiently. Here a semi-supervised learning approach is implemented on both labeled and unlabeled data. This system clearly analyzed both destination feature data set and source data set. After so many experiments, we are able to achieve 97% reduction of false alarm rate which significantly improves the efficiency.

NIDS, MVPSys, False alarm rate, Accuracy, WEKA, Snort, DARPA

"A Practical Approach to Reduce False Alarm Rate in Network Intrusion Detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.387-401, June-2018, Available :http://www.jetir.org/papers/JETIR1806445.pdf

"A Practical Approach to Reduce False Alarm Rate in Network Intrusion Detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp387-401, June-2018, Available at : http://www.jetir.org/papers/JETIR1806445.pdf