Open Access
Subscription Access
Open Access
Subscription Access
Qualitative Risk Analysis through Anomaly Detection by Two Pass Clustering Technique
Subscribe/Renew Journal
Risk analysis is the way of preventing the damage by the prevention mechanism. The proper risk analysis techniques employed will reduce the vulnerabilities, threats, impacts to a great extent. Here the risk management of information security is dealt with the anomaly detection mechanism. The Anomaly detection is carried out by the data clustering to find the outlier as the anomaly. The clustering process is enhanced by the blended mechanism of genetic algorithms and the Ant Colony Optimization. The quality of the clusters obtained are improved and demonstrated with the results. The proposed Anomaly detection process is compared with two other methods and the results obtained are appreciable.
Keywords
Information Security, Risk Analysis, Anomaly Detection, Clustering
Subscription
Login to verify subscription
User
Font Size
Information
- Vishal Bhatnagar, Sanur Sharma, Data Mining: A Necessity for Information Security, Journal of Knowledge Management Practice, Vol. 13, No. 1, March 2012
- Technical Department of ENISA Section,” Risk ManagementRisk Management - Principles and Inventories for Risk Management / Risk Assessment methods and tools”, June, 2006
- Straub, D.W. and Welke, R.J. Coping with systems risk: Security planning models for management decision making. MIS Q. 22, 4 (1998), 441–469.
- Michael E. Whitman, “Enemy at the gate: threats to information security”, Communications of the ACM August 2003/Vol. 46, No. 8
- David Kim; Michael G. Solomon, “Fundamentals of Information Systems Security”, November, 2010
- Fenz, Stefan; Ekelhart, Andreas; and Neubauer, Thomas "Information Security Risk Management: In Which Security Solutions Is It Worth Investing?," Communications of the Association for Information Systems: Vol. 28, Article 22, 2011.
- Andy Ju An Wang: Information security models and metrics. ACM Southeast Regional Conference (2) 2005: 178-184
- http://en.wikipedia.org/wiki/Policy
- Tipton, H. (Ed.) (2003). Information Security Management Handbook. Boca Raton, FL: Auerbach Publishers, Inc.
- Jian Pei, Shambhu, J. Upadhyaya, Faisal Farooq and Venugopal Govindaraju , “Data Mining for Intrusion Detection – Techniques, Applications and Systems” , Data Mining Techniques for Intrusion Detection and Computer Security, University at Buffalo, New York.
- Varun Chandola, Arindam Banerjee, and Vipin Kumar, “Anomaly Detection: A Survey”, ACM Computing Surveys, Vol. 41, No. 3, Article 15, July 2009.
- Lee, W., Stolfo, S., “Data Mining Approaches for Intrusion Detection”, Proceedings of the 7th USENIX Security Symposium, pp. 79-94, 1998.
- Barbara D, Couto J, Jajodia S, and Wu N, “ADAM: A Testbed for Exploring the Use of Data Mining in Intrusion Detection”, SIGMOD Record, Vol. 30, No. 4, pp. 15-24, 2001.
- Lee, W. Stolfo and S. Kui, M., “A Data Mining Framework for Building Intrusion Detection Models”, IEEE Symposium on Security and Privacy, pp.120-132, 1999.
- Manganaris S, Christensen M, Zerkle D, and Hermiz K, “A Data Mining Analysis of RTID Alarms”, Proceedings of Recent Advances in Intrusion Detection, Second International Workshop, 1999.
- Margaret H. Dunham, “Data Mining Introductory and Advanced Topics”, Prentice Hall, 2003, ISBN 0-13-088892-3
- Arman Tajbakhsh, Mohammad Rahmati, and Abdolreza Mirzaei, “Intrusion detection using fuzzy association rules”, Applied Soft Computing, Vol. 9, pp. 462–469, 2009.
- Mohammadreza Ektefa, Sara Memar, Fatimah Sidi , and Lilly Suriani Affendey, “Intrusion Detection Using Data Mining Techniques Information Retrieval & Knowledge Management”, CAMP, 2010.
- M. Sathya Narayana, B. V. V. S. Prasad, A. Srividhya and K. Pandu Ranga Reddy, “Data Mining Machine Learning Techniques – A Study on Abnormal Anomaly Detection System”, International Journal of Computer Science and Telecommunications ,Vol. 2, Issue 6, September 2011.
- J. E. Dickerson and J. A. Dickerson, “Fuzzy network profiling for intrusion detection” , In 19th International Conference of the North American Fuzzy Information Processing Society (NAFIPS), Atlanta, GA, pp. 301 – 306 , 2000 Yasser Yasami, Saadat Pour Mozaffari, (2010), “A Novel Unsupervised Classification Approach for Network Anomaly Detection by K-Means Clustering and ID3 Decision Tree Learning Methods”, Springer, Journal of Supercomputing, vol. 53(1), pp. 231-245.
- Sandhya Pedabachigari, Ajith Abraham, Crina Grosan, Jhonson Thomas, (2007), “ Modeling Intrusion Detection System using Hybrid Intelligent Systems ”, Elsevier, Journal of Network and Computer Applications, vol. 30(1), pp. 114-132.
- A.S. Aneetha , S.Bose, (2012), “The combined approach for anomaly detection using neural networks and clustering techniques”, Computer Science & Engineering: An International Journal (CSEIJ), Vol.2, No.4, August 2012
- C.Immaculate mary, S.V.Kasmir raja, “Refinement of clusters from k-means with ant colony optimization”, Journal of Theoretical and Applied Information Technology, 2005.
Abstract Views: 516
PDF Views: 0