INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Data Mining Approaches For Network Intrusion Detection System
Authors Name:
T.S.Meenatchi
, K. Mythili , M. Gayathri
Unique Id:
IJSDR1609029
Published In:
Volume 1 Issue 9, September-2016
Abstract:
With the rapidly development of internet, Network Security has become the key issue of network based services and information sharing on networks. Intruders are monitoring computer network continuously for attacks. Intrusion happens simply when the security and privacy of a system is compromised. To protect this, a sophisticated firewall with efficient intrusion detection system (IDS) is required to prevent computer network from attacks. Intrusion Detection System (IDS) plays very important role in network security as it detects various types of attacks in network. An effective Intrusion detection system requires high accuracy rate (True positive) and low false alarm (False Positive and False Negative) rate. In this paper data mining techniques SMO (Support Vector Machine), IBk (k-Nearest Neighbour), Attribute Selected classifier(Meta) ,J48 (Tree) and KDD99 data set is used to evaluate these Data mining algorithms which is most efficient and high accuracy rated.
Keywords:
Cite Article:
"Data Mining Approaches For Network Intrusion Detection System", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.1, Issue 9, page no.195 - 200, September-2016, Available :http://www.ijsdr.org/papers/IJSDR1609029.pdf
Downloads:
000346977
Publication Details:
Published Paper ID: IJSDR1609029
Registration ID:160783
Published In: Volume 1 Issue 9, September-2016
DOI (Digital Object Identifier):
Page No: 195 - 200
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
Facebook Twitter Instagram LinkedIn