DATALEAK: Data Leakage Detection System

Open access

Abstract

Data leakage is an uncontrolled or unauthorized transmission of classified information to the outside. It poses a serious problem to companies as the cost of incidents continues to increase. Many software solutions were developed to provide data protection. However, data leakage detection systems cannot provide absolute protection. Thus, it is essential to discover data leakage as soon as possible. The purpose of this research is to design and implement a data leakage detection system based on special information retrieval models and methods. In this paper a semantic informationretrieval based approach and the implemented DATALEAK application is presented.

[1] A. Shabtai, Y. E. Asaf, and R. Lior, A survey of data leakage detection and prevention solutions. Springer, 2012, ISBN: 978-1-4614-2052-1.

[2] A. Skrop, “Data Leakage Detection Using Information Retrieval Methods.” In: IMMM 2014, The Fourth International Conference on Advances in Information Mining and Management, pp. 74-78, 2014.

[3] C. T. Meadow, Text Information Retrieval Systems. Academic Press, 2000, ISBN: 0124874053.

[4] E. Gessiou, Q. H. Vu, and S. Ioannidis, “IRILD: an Information Retrieval based method for Information Leak Detection,” In Proceedings of European Conference on Computer Network Defense, 2011, pp. 33-40, IEEE.

[5] P. Papadimitriou and H. Garcia-Molina, “Data leakage detection," Knowledge and Data Engineering, IEEE Transactions on, vol. 23(1), 2011, pp. 51-63.

[6] R. Baeza-Yates and B. Ribeiro-Neto, Modern information retrieval: The Concepts and Technology behind Search (2nd Edition). ACM Press Books, Addison-Wesley Professional, 2011, ISBN: 0321416910.

[7] S. Dominich, "Connectionist interaction information retrieval," Information processing & management, vol. 39.2, 2003, pp. 167-193, doi: 10.1016/S0306-4573(02)00046-8.

[8] S. Dominich, "Interaction information retrieval," Journal of Documentation, vol. 50.3, 1994, pp. 197-212, doi: 10.1108/eb026930.

[9] S. Dominich, A. Skrop, and Zs. Tuza, “Formal Theory of Connectionist Web Retrieval,” Soft Computing in Web Information Retrieval, Studies in Fuzziness and Soft Computing, vol. 197, 2006, pp. 163-194.

[10] W. B. Croft, D.Metzler, and T. Strohman, Search engines: Information retrieval in practice (p. 283). Reading: Addison-Wesley, 2010.

[11] Y. Liu, C. Corbett, K. Chiang, R. Archibald, B. Mukherjee, and D. Ghosal, “SIDD: A framework for detecting sensitive data exfiltration by an insider attack,” In System Sciences, 2009, HICSS'09, pp. 1-10, IEEE.

MACRo 2015

Proceedings of the 5th International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics

Journal Information

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 83 83 13
PDF Downloads 19 19 4