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A Study of Intrusion Detection System using Efficient Data Mining Techniques
P. Rutravigneshwaran1
1 Department of Computer Application, Rev. Jacob Memorial Christian College, Ambilikkai, India.
Correspondence should be addressed to:
Section:Review Paper, Product Type: Journal
Vol.5 , Issue.6 , pp.5-8, Dec-2017


Online published on Dec 31, 2017
Copyright © P. Rutravigneshwaran . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Citation :
IEEE Style Citation: P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques”, International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.6, pp.5-8, 2017.

MLA Style Citation: P. Rutravigneshwaran "A Study of Intrusion Detection System using Efficient Data Mining Techniques." International Journal of Scientific Research in Network Security and Communication 5.6 (2017): 5-8.

APA Style Citation: P. Rutravigneshwaran, (2017). A Study of Intrusion Detection System using Efficient Data Mining Techniques. International Journal of Scientific Research in Network Security and Communication, 5(6), 5-8.
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Abstract :
IDS is a software consequence monitors the humiliation or behavior plus investigate any immoral operation suggest itself. Fantastic increase and tradition of internet raises concerns in relation to how to defend and communicate the digital in order in a safe approach. Nowadays, hackers use different types of attacks for getting the valuable information. IntheproposedFast Hierarchical Relevance Vector Machine (FHRVM), AnalyticalHierarchy ProcessMethod (AHP) isusedtoselect the inputweightsandhiddenbiases. Simulation has been carried out using Math works MATLAB R2012a. KDD Cup 1999 dataset istakenfor testingthe performanceoftheproposedworkandtheresults indicate that FHRVM has achieved higher detectionrate and lowfalse alarmrate thanthat ofexistingSVMalgorithm. This research evaluate the efficiency of machine learning methods in intrusion detection system, together with classification tree and support vector machine, with the expect of given that reference for establishing intrusion detection system in future. Compared with further interrelated works in data mining-based intrusion detectors accuracy, detection rate, false alarm rate. It moreover show improved act than KDD Winner, particularly used for two types of attacks namely, U2R type and R2L type. Comparison results of C4.5, SVM. we finds that C4.5 is superior to SVM in accuracy and detection; in accuracy for Probe,Dos and U2R attacks, C4.5 is also better than SVM and FHRVM; but in false alarm rate FHRVM is better. In this paper enhance that FHRVM is better than c4.5 and SVM for U2R attack & R2L attack.
Key-Words / Index Term :
Classification tree, SVM, FHRVM, Internet attack, Intrusion detection system (IDS)
References :
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[3] MahbodTavallaee, IbrahimBagheri, Wei Lu, and Ali A. Ghorbanifar A Detailed Analysis of the KDD CUP 99 Data Set proceeding of the 2009 IEEE symposium on computational Intelligence in security and defense application .
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[12] KDD Cup 1999. Available on:, Ocotber 2007.
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