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Intrusion Detection using a Novel Hybrid Method Incorporating an Improved KNN

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Hossein Shapoorifard, Pirooz Shamsinejad
10.5120/ijca2017914340

Hossein Shapoorifard and Pirooz Shamsinejad. Intrusion Detection using a Novel Hybrid Method Incorporating an Improved KNN. International Journal of Computer Applications 173(1):5-9, September 2017. BibTeX

@article{10.5120/ijca2017914340,
	author = {Hossein Shapoorifard and Pirooz Shamsinejad},
	title = {Intrusion Detection using a Novel Hybrid Method Incorporating an Improved KNN},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2017},
	volume = {173},
	number = {1},
	month = {Sep},
	year = {2017},
	issn = {0975-8887},
	pages = {5-9},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume173/number1/28297-2017914340},
	doi = {10.5120/ijca2017914340},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

These days, with the tremendous growth of network-based service and shared information on networks, the risk of network attacks and intrusions increases too, therefore network security and protecting the network is getting more significance than before. Intrusion Detection System (IDS) is one of the solutions to detect attacks and anomalies in the network. The ever rising new intrusion or attack types causes difficulties for their detection, therefore Data mining techniques has been widely applied in network intrusion detection systems for extracting useful knowledge from large number of network data to detect intrusions. Many clustering and classification algorithms are used in IDS, therefore improving the functionality of these algorithms will improve IDS performance. This paper focuses on improving KNN classifier in existing intrusion detection task which combines K-MEANS clustering and KNN classification.

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Keywords

Intrusion Detection System; Data Mining; Network Security; Clustering; IDS system; K-MEANS; K- nearest neighbor; K- farthest neighbor; CANN.