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Intrusion Detection System using Classification Technique

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Rajesh Wankhede, Vikrant Chole
10.5120/ijca2016909397

Rajesh Wankhede and Vikrant Chole. Article: Intrusion Detection System using Classification Technique. International Journal of Computer Applications 139(11):25-28, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Rajesh Wankhede and Vikrant Chole},
	title = {Article: Intrusion Detection System using Classification Technique},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {11},
	pages = {25-28},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

In today’s world people are extensively using internet and thus are also vulnerable to its flaws. Cyber security is the main area where these flaws are exploited. Intrusion is one way to exploit the internet for search of valuable information that may cause devastating damage, which can be personal or on a large scale. Thus Intrusion detection systems are placed for timely detection of such intrusion and alert the user about the same. Intrusion Detection using hybrid classification technique consist of a hybrid model i.e. misuse detection model (AdTree based) and Anomaly model (svm based).NSL-KDD intrusion detection dataset plays a vital role in calibrating intrusion detection system and is extensively used by the researchers working in the field of intrusion detection. This paper presents Association rule mining technique for IDS.

References

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Keywords

SVM, AdTree, NSL-KDD