A Survey on Areas in Data Mining for Intrusion Detection
Abstract
Intrusion detection is the act of detecting activities that compromise the confidentiality, integrity and availability of a resource. Data Mining is the process of analyzing huge amounts of data to obtain useful information for the required cause. This paper presents various techniques used to detect intrusions along with their pros and cons. An efficient detection method must provide proper diagnosis of any obstruction with greater accuracy and low false alarm rate.
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Reema Patel, Amit Thakkar, Amit Ganatra,” A Survey and Comparative Analysis of Data Mining Techniques for Network Intrusion Detection Systems”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-1, March 2012.
Manasi Gyanchandani, J.L.Rana, R.N.Yadav,” Taxonomy of Anomaly Based Intrusion Detection System: A Review”, International Journal of Scientific and Research Publications ISSN 2250-3153, Volume 2, Issue 12, December 2012.
RavindraThool, Kapil Wankhade, Sadia Patka,” An Overview of Intrusion Detection Based on Data Mining Techniques”, International Conference on Communication Systems and Network Technologies, 2013.
Poonam Dabas, Rashmi Chaudhary, “Survey of Network Intrusion Detection Using K-Mean Algorithm”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 3, March 2013.
Amanpreet Chauhan, Gaurav Mishra, Gulshan Kumar,” Survey on Data Mining Techniques in Intrusion Detection”, International Journal of Scientific & Engineering Research, Volume 2, Issue 7, July-2011.
Z. Muda, W. Yassin, M.N. Sulaiman, N. I Udzir, ”A K-Means and Naïve Bayes Approach for Better Intrusion Detection”, Information Technology Journal, 648-655, 2011.
Brian Laing, “How To Guide-Implementing a Network Based Intrusion Detection System”.
Harshna , Navneet Kaur,” Survey paper on Data Mining techniques of Intrusion Detection”, International Journal of Science, Engineering and Technology Research (IJSETR) , Volume 2, Issue 4, April 2013.
Theodoros Lappas , Konstantinos Plechrinis,” Data mining techniques for (Network ) Intrusion Detection Systems”.
Yousef Emami, Marzieh Ahmadzadeh,Mohammad Salehi,Sajad Homayoun ,” Efficient Intrusion Detection using Weighted K-means Clustering and Naïve Bayes Classification”, Journal of Emerging Trends in Computing and Information Sciences, Vol. 5, No. 8 August 2014.
Neha Jain, Shikha Sharma, “The Role of Decision Tree Technique for Automating Intrusion Detection System “, International Journal of Computational Engineering Research (ijceronline.com), Vol. 2 Issue 4.
Suresh Kashyap, Pooja Agrawal, Vikas Chandra Pandey, Suraj Prasad Keshri,” Soft Computing Based Classification Technique Using KDD 99 Data Set for Intrusion Detection System “,International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 4, April 2013 .
Pankaj Saxena , Vineeta Singh ,Sushma Lehri,” Evolving Efficient Clustering Patterns in Liver Patient Data through Data Mining Techniques , International Journal of Computer Applications , Volume 66– No.16, March 2013.
Ravi Ranjan, G. Sahoo,” A New Clustering Approach For Anomaly Intrusion Detection”, International Journal of Data Mining & Knowledge Management Process (IJDKP) ,Vol.4, No.2, March 2014.
http://technbyte.blogspot.in/2011/11/what-is-intrusion-detection-syste-m.html.
An Approach for Intrusion Detection Based Information Technology Essay:http://www.ukessays.com/essays/information-technology/an-approach-for-intrusion-detection-based-information-technology-essay.php.
Syracuse University Lecture Notes for Internet Security, “Intrusion Detection System”, Fall 2006.
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