Data Mining For Security Purposes

Authors

  • V. Maria Antoniate Martin  Research Scholar, Department of Computer Science, Research and Development Centre, Bharathiar University, Coimbatore, Tamil Nadu, India
  • Dr. K. David  Assistant Professor, Department of Computer Science, The Rajah’s College, Pudukkottai, Tamil Nadu, India
  • A. Paulin Jenifer  Student, Department of Information Technology, St. Joseph’s College, Trichy, Tamil Nadu, India

Keywords:

Data Mining, Security, Data Quality, Integrity

Abstract

The integrity of computer networks, both in relation to security and with regard to the institutional life of the nation in general, is a growing concern. Security and defense networks, proprietary research, intellectual property, and data based market mechanisms that depend on unimpeded and undistorted access, can all be severely compromised by malicious intrusions. We need to find the best way to protect these systems. In addition, we need techniques to detect security breaches. Data mining has many applications in security including in national security (e.g., surveillance) as well as in cyber security (e.g., virus detection). The threats to national security include attacking buildings and destroying critical infrastructures such as power grids and telecommunication systems. Data mining techniques are being used to identify suspicious individuals and groups, and to discover which individuals and groups are capable of carrying out terrorist activities. Data mining is also being applied to provide solutions such as intrusion detection and auditing. In this paper, we will focus mainly on data mining for security purpose.

References

  1. Mafruz Zaman Ashrafi, David Taniar, Kate A. Smith, ”Data Mining Architecture for Clustered Environments” , Proceeding PARA '02 Proceedings of the 6th International Conference on Applied Parallel Computing Advanced Scientific Computing, Pages 89-98, Springer-Verlag London, UK ©2002
  2. Z. Ferdousi, A. Maeda, “Unsupervised outlier detection in time series data”, 22nd International Conference on Data Engineering Workshops, pp. 51-56, 2006
  3. Morgenstern, M., “Security and Inference in Multilevel Database and Knowledge Base Systems,” Proceedings of the ACM SIGMOD Conference, San Francisco, CA, June 1987.
  4.  S. A. Demurjian and J. E. Dobson, “Database Security IX Status and Prospects Edited by D. L. Spooner ISBN 0 412 72920 2, 1996, pp. 391- 399.
  5.  Lin, T. Y., “Anamoly Detection -- A Soft Computing Approach”, Proceedings in the ACM SIGSAC New Security Paradigm Workshop, Aug 3-5, 1994,44-53.,1994
  6. Scott W. Ambler, “Challenges with legacy data: Knowing your data enemy is the first step in overcoming it”, Practice Leader, Agile Development, Rational Methods Group, IBM, 01 Jul 2001.
  7. Agrawal, R, and R. Srikant, “Privacy-preserving Data Mining,” Proceedings of the ACM SIGMOD Conference, Dallas, TX, May 2000.
  8. Clifton, C., M. Kantarcioglu and J. Vaidya, “Defining Privacy for Data Mining,” Purdue University, 2002 (see also Next Generation Data Mining Workshop, Baltimore, MD, November 2002.
  9.  Evfimievski, A., R. Srikant, R. Agrawal, and J. Gehrke, “Privacy Preserving Mining of Association Rules,” In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada, July 2002.
  10. Fung B., Wang K., Yu P. ”Top-Down Specialization for Information and Privacy Preservation. ICDE Conference, 2005.
  11. Wang K., Yu P., Chakraborty S., “ Bottom-Up Generalization: A Data Mining Solution to Privacy Protection.”, ICDM Conference, 2004.
  12. Agrawal, R., Srikant, R.: Privacy-Preserving Data Mining. In: SIGMOD Conference, pp.439–450 (2000)
  13. Kantarcioglu, M., Clifton, C.: Privately Computing a Distributed k-nn Classifier. In: Bou-licaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS, vol. 3202,279–290. Springer, Heidelberg (2004)
  14.  Kantarcioglu, M., Kardes, O.: Privacy-Preserving Data Mining Applications in the Mali-cious Model. In: ICDM Workshops, pp. 717–722 (2007)
  15. Liu, L., Kantarcioglu, M., Thuraisingham, B.M.: The applicability of the perturbation based privacy preserving data mining for real-world data. Data Knowl. Eng. 65(1), 5–21 (2008)
  16. Liu, L., Kantarcioglu, M., Thuraisingham, B.M.: A Novel Privacy Preserving Decision Tree. In: Proceedings Hawaii International Conf. on Systems Sciences (2009)
  17. Fayyad, U., Piatetsky-Shapiro, G., and Smyth P., “From Data Mining toKnowledge Discovery in Databases,” AI Magazine, AmericanAssociation for Artificial Intelligence, 1996.
  18. Larose, D. T., “Discovering Knowledge in Data: An Introduction to DataMining”, ISBN 0-471-66657-2, ohn Wiley & Sons, Inc, 2005.

Downloads

Published

2018-04-30

Issue

Section

Research Articles

How to Cite

[1]
V. Maria Antoniate Martin, Dr. K. David, A. Paulin Jenifer, " Data Mining For Security Purposes, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.1490-1498, March-April-2018.