Information leakage detection and protection of leaked information by using the MAC-IP binding technique

  • Authors

    • B. Raja Koti
    • G.V.S. Raj Kumar
    2018-02-05
    https://doi.org/10.14419/ijet.v7i1.7.10700
  • AES, Data leakage, Data Protection, Data Privacy, Guilt Agent, MAC-IP Binding.
  • The Digital world is advancing in terms of technological development day by day, resulting in an instantaneous rise in Data. This massive amount of Data has introduced the thought of Big Data, which has attracted both the business and IT sectors leaving the scope for huge opportunities. In turn, securing this massive data has become a challenging issue in the field of Information and Communication Technology. In this paper, we have carried out the work on business information sharing data which contains some sensitive information to investigate the security challenges of data in the field of business communication. The article an attempt is also made to identify the user’s intention or behavior during the navigation of data. The greatest challenge that is associated here is to prevent the integrity of the data while sharing the data from organization to the third party, where there exist huge chances of data loss, leakages or alteration. This paper highlights the concepts of data leakage, the techniques to detect the data leakage and the process of protecting the leaked data based on encrypted form.

  • References

    1. [1] M. Alazab and R. Broadhurst Spam and criminal activity, Trends & issues in crime and criminal justice no. 526, Australian Institute of Criminology, 2016.

      [2] H.M.J Almohri, L.T. Watson, D.F. Yao and X.M Ou, Security optimization of dynamic networks with probabilistic graph modeling and linear programming, IEEE Transactions on Dependable and Secure Computing, IEEE, Vol. 13, No. 4, 2016, 474-487.

      [3] A. Carnielli, M. Aiash, M. Alazab, and others, on preserving privacy in cloud computing using ToR, London; San Diego; Cambridge, USA: Elsevier, 2016.

      [4] S. Pearson, “Privacy, security and trust in cloud computing,†in Privacy and Security for Cloud Computing (S. Pearson and G. Yee, eds.), Computer Communications and Networks, pp. 3–42, Springer London, 2013.

      [5] National Institute Standards and Technology (NIST) (2013), Glossary of Key Information Security Terms, NISTIR 7298 Revision 2, NIST, Gaithersburg, MD.

      [6] “Sensitive Data Classification and Protection†Overcoming the Challenges to Classify and Protect Sensitive Data at Federal Government Agencies.

      [7] https://www.gpo.gov/fdsys/pkg/USCODE-2010-title5/pdf/USCODE-2010-title5-partI-chap5-subchapII-sec552a.pdf

      [8] B. Raja Koti, Dr. G.V.S. Raj Kumar, Dr. Y. Srinivas, "A Comprehensive Study and Comparison of Various Methods on Data Leakages", International Journal of Advanced Research in Computer Science, Volume 8, No.7, July – August 2017, pp-627-631.

      [9] Panagiotis Papadimitriou, "Data Leakage Detection", IEEE Transactions on Knowledge and Data Engineering, Vol. 23.

      [10] P. Papadimitriou and H. Garcia-Molina, "Data leakage detection", Technical report, Stanford University, 2008.

      [11] Blakley, B., Mcdermott, E. and Geer, D. (2001), Information Security is Information Risk Management, Proceedings of the 2001 Workshop on New Security Paradigms, ACM, Cloudcroft, New Mexico, pp. 97-104.

      [12] Gordon, L.A. and Loeb, M.P. (2002), “The economics of information security investmentâ€, ACM Transactions on Information and System Security (TISSEC), Vol. 5 No. 4, pp. 438-457.

      [13] Anderson, R. (2001), “Why information security is hard-an economic perspectiveâ€, Computer Security Applications Conference, ACSAC 2001. Proceedings 17th Annual 2001, IEEE, pp.358-365.

      [14] Alberts, C.J. and Dorofee, A. (2002), Managing Information Security Risks: The OCTAVE Approach, Addison-Wesley Longman Publishing Co. Inc., Boston, MA.

      [15] Alberts, C., Dorofee, A., Stevens, J. and Woody, C. (2003), Introduction to the OCTAVE Approach, Carnegie Mellon University, Pittsburgh, PA.

      [16] Gordon, L., Loeb, M. and Lucyshyn, W. (2003), “Information security expenditures and real options: a wait-and-see approachâ€, Computer Security Journal, Vol. 19 No. 2, pp. 1-7.

      [17] Siponen, M.T. and Oinas-Kukkonen, H. (2007), “A review of information security issues and respective research contributionsâ€, ACM Sigmis Database, Vol. 38 No. 1, pp. 60-80.

      [18] Chen, Y. (2005), “Information valuation for information lifecycle managementâ€, Autonomic Computing, ICAC 2005. Second International Conference on 2005, IEEE, pp.135-146.

      [19] Sokolowski, J.A. and Banks, C.M. (2012), Handbook of Real-World Applications in Modeling and Simulation, John Wiley & Sons, Hoboken, NJ.

      [20] Symantec. (2015). Symantec Data Loss Prevention. [Online]. Available: http://www.symantec.com/data-loss-prevention.

      [21] Identity Finder. [Online]. Available: http://www.identityfinder.com

      [22] Global Velocity Inc. (2015). Cloud Data Security from the Inside Out—Global Velocity. [Online]. Available: http://www.globalvelocity.com

      [23] GTB Technologies Inc. (2015). GoCloudDLP. [Online]. Available: http://www.goclouddlp.com.

      [24] B Raja Koti, GVS Raj Kumar, Y Srinivas, “Identification of Guilt Agent and Leaked Data by Using MAC-IPâ€, International Journal of Applied Engineering Research, 2017, Volume 12, Issue 22, pp 12237-12245.

      [25] B. Hauer, "Data and Information Leakage Prevention within the Scope of Information Security," in IEEE, vol. 3, pp. 2554-2565, 2015.

      [26] Jaymala Chavan and Priyanka Desai “Data Leakage Detection Using Data Allocation Strategies†International Journal of Advance in Engineering and Technology (IJAET), Volume 6 issue 6, Nov 2013.

      [27] B. Hauer, "Data and Information Leakage Prevention within the Scope of Information Security," in IEEE Access, vol. 3, no. , pp. 2554-2565, 2015.

      [28] Sandip A. Kale C, Prof.S.V. Kulkarni C, “Data Leakage Detection: A Surveyâ€, IOSR Journal of Computer Engineering ISSN: 2278-0661 Volume 1, Issue 6 (July-Aug 2012), PP 32-35.

      [29] Q. B. Hani and J. P. Dichter, "Data leakage presentation using homomorphic encryption in cloud computing," 2016 IEEE Long Island Systems, Applications and Technology Conference, Farmingdale, NY, 2016, pp. 1-5.

      [30] DivyaChaube, Sonali Gandhi, Priyanka Gupta, “Implementation of Guilt Model and Allocation Strategy for Data Leakage Detectionâ€, International Journal of Scientific and Research, Volume 5, Issue 4, April 2015, ISSN 2250-3153.

      [31] S. Peneti and B. P. Rani, "Data leakage prevention system with time stamp," 2016 International Conference on Information Communication and Embedded Systems , Chennai, 2016, pp 1-4.

      [32] C. Suresh Kumar, K. Iyakutti, Semantic Cluster-Based Classification for Data Leakage Detection for the Cloud Security, International Journal of Computer Applications, ISSN: 0975-8887, Vol 110 – No 6, January 2015.

      [33] National Institute of Standards and Technology FIPS 197 Advanced Encryption Standard (AES) Published: November 2001.

      [34] R. Tahboub and Y. Saleh, "Data Leakage/Loss Prevention Systems (DLP)," 2014 World Congress on Computer Applications and Information Systems (WCCAIS), Hammamet, 2014, pp. 1-6.

      [35] S. Liu and R. Kuhn, "Data Loss Prevention," in IT Professional, vol. 12, no. 2, March-April 2010, pp. 10-13.

      [36] G. Lawton, "New Technology Prevents Data Leakage," in Computer, vol. 41, no. 9, Sept. 2008, pp. 14-17.

      [37] D. Kolevski and K. Michael, "Cloud computing data breaches a socio-technical review of literature," 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), Noida, 2015, pp. 1486-1495.

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  • How to Cite

    Koti, B. R., & Kumar, G. R. (2018). Information leakage detection and protection of leaked information by using the MAC-IP binding technique. International Journal of Engineering & Technology, 7(1.7), 230-235. https://doi.org/10.14419/ijet.v7i1.7.10700