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A Survey on Data Mining Approaches for Network Intrusion Detection System

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Anirudha A. Kolpyakwar, Mangesh G. Ingle, Ritesh V. Deshmukh
10.5120/ijca2017912615

Anirudha A Kolpyakwar, Mangesh G Ingle and Ritesh V Deshmukh. A Survey on Data Mining Approaches for Network Intrusion Detection System. International Journal of Computer Applications 159(1):20-23, February 2017. BibTeX

@article{10.5120/ijca2017912615,
	author = {Anirudha A. Kolpyakwar and Mangesh G. Ingle and Ritesh V. Deshmukh},
	title = {A Survey on Data Mining Approaches for Network Intrusion Detection System},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {159},
	number = {1},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {20-23},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume159/number1/26967-2017912615},
	doi = {10.5120/ijca2017912615},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Data mining has been gaining popularity in knowledge discovery field, particularity with the increasing availability of digital documents in various languages from all around the world. Network intrusion detection is the process of monitoring the events occurring in a computing system or network and analysing them for signs of intrusions. In this paper, intrusion detection & several areas of intrusion detection in which data mining technology applied are discussed. Data mining techniques are used to discover consistent and useful patterns of system features that describe program and user behaviour. Data mining can improve variant detection rate, control false alarm rate and reduce false dismissals. By using these set of relevant system features to compute classifiers that recognize anomalies & known intrusion.

References

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Keywords

Intrusion Detection, Data Mining, Misuse Detection, Anomaly Detection.