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Title: :  Stream Data Mining Classification for an efficient Anomaly Intrusion Detection
PaperId: :  2255
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 2 Issue 3 2016
DUI:    16.0415/IJARIIE-2255
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Mr. Ravi JethvaL.J. Institute of Engineering & Technology, Gujarat Technological University

Abstract

Computer Engineering
Data Mining, IDS, Anomaly Detection, GNP, Fuzzy Rule Mining, Probability Density Function
Intrusion Detection System using Data Mining algorithms is a wide scope of Research. Wherein, various classification techniques can be used for a better classification of Known and Unknown type of attacks. An IDS (Intrusion Detection System) monitors the network traffic and then sends the suspicious activity reports to the System Administrator. In order to improve the efficiency of classification, various different techniques such as GNP, Fuzzy class Association, Hoeffding Tree Algorithm and Neural Network algorithm are used, but they fall short on some or other factors. So, in our work we’ve proposed and implemented a combination of Fuzzy GNP Association Rule Mining along with Probability Density Function which overcome the problems of sub-attribute utilization problem and is efficient in terms of time taken in classification as well as reduces False Alarms and improves Detection Ratio.

Citations

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IJARIIE Mr. Ravi Jethva. "Stream Data Mining Classification for an efficient Anomaly Intrusion Detection" International Journal Of Advance Research And Innovative Ideas In Education Volume 2 Issue 3 2016 Page 536-543
MLA Mr. Ravi Jethva. "Stream Data Mining Classification for an efficient Anomaly Intrusion Detection." International Journal Of Advance Research And Innovative Ideas In Education 2.3(2016) : 536-543.
APA Mr. Ravi Jethva. (2016). Stream Data Mining Classification for an efficient Anomaly Intrusion Detection. International Journal Of Advance Research And Innovative Ideas In Education, 2(3), 536-543.
Chicago Mr. Ravi Jethva. "Stream Data Mining Classification for an efficient Anomaly Intrusion Detection." International Journal Of Advance Research And Innovative Ideas In Education 2, no. 3 (2016) : 536-543.
Oxford Mr. Ravi Jethva. 'Stream Data Mining Classification for an efficient Anomaly Intrusion Detection', International Journal Of Advance Research And Innovative Ideas In Education, vol. 2, no. 3, 2016, p. 536-543. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Stream_Data_Mining_Classification_for_an_efficient_Anomaly_Intrusion_Detection_ijariie2255.pdf (Accessed : ).
Harvard Mr. Ravi Jethva. (2016) 'Stream Data Mining Classification for an efficient Anomaly Intrusion Detection', International Journal Of Advance Research And Innovative Ideas In Education, 2(3), pp. 536-543IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Stream_Data_Mining_Classification_for_an_efficient_Anomaly_Intrusion_Detection_ijariie2255.pdf (Accessed : )
IEEE Mr. Ravi Jethva, "Stream Data Mining Classification for an efficient Anomaly Intrusion Detection," International Journal Of Advance Research And Innovative Ideas In Education, vol. 2, no. 3, pp. 536-543, May-Jun 2016. [Online]. Available: http://ijariie.com/AdminUploadPdf/Stream_Data_Mining_Classification_for_an_efficient_Anomaly_Intrusion_Detection_ijariie2255.pdf [Accessed : ].
Turabian Mr. Ravi Jethva. "Stream Data Mining Classification for an efficient Anomaly Intrusion Detection." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 2 number 3 ().
Vancouver Mr. Ravi Jethva. Stream Data Mining Classification for an efficient Anomaly Intrusion Detection. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2016 [Cited : ]; 2(3) : 536-543. Available from: http://ijariie.com/AdminUploadPdf/Stream_Data_Mining_Classification_for_an_efficient_Anomaly_Intrusion_Detection_ijariie2255.pdf
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