Data Mining Approaches For Network Intrusion Detection System
T.S.Meenatchi
, K. Mythili , M. Gayathri
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.
"Data Mining Approaches For Network Intrusion Detection System", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.1, Issue 9, page no.195 - 200, September-2016, Available :https://ijsdr.org/papers/IJSDR1609029.pdf
Volume 1
Issue 9,
September-2016
Pages : 195 - 200
Paper Reg. ID: IJSDR_160783
Published Paper Id: IJSDR1609029
Downloads: 000347060
Research Area: Engineering
Country: Unknown, Unknown, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
