Abstract
The aim of enabling the use of illegally obtained money for legal purposes, while hiding the true source of the funds from government authorities has given rise to suspicious transactions. Illegal transactions are detected using data mining and statistical techniques with the input data like various suspicious reports or the data set of all transactions within a financial institution. The output obtained is the set of highly suspicious transactions or highly suspicious entities (e.g., persons, organizations, or accounts). In this paper, we propose a database forensics methodology to monitor database transactions through audit logs. The Rule-based Bayesian Classification algorithm is applied to determine undetected illegal transactions and predicting initial belief of the transactions to be suspicious. Dempster-Shafer’s theory of evidence is applied to combine different parameters of the transactions obtained through audit logs to verify the uncertainty and risk level of the suspected transactions. Thus a framework is designed and developed which can be used as a tool for the digital investigators.
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RBI Rules and Monitoring Transactions. https://rbi.org.in. Accessed 2 June 2018
Health Insurance Portability and Accountability Act. http://www.cms.gov/HIPAAGenInfo/
SOX, Sarbanes Oxley Audit Requirements. http://www.sarbanes-oxley-101.com/sarbanes-oxley-audits.htm Accessed 23 July 2017
Sentz, K., Ferson, S.: Combination of Evidence in Dempster-Shafer Theory. Sandia National Laboratories (2002)
Badal-Valero, E., Alvarez-Jareño, J.A., Pavía, J.M.: Combining Benford’s Law and machine learning to detect money laundering. An actual Spanish court case. Forensic Sci. Int. 282, 24–34 (2018)
Kuna, H.D., Matinez, R.G., Villatoro, F.R.: Outlier detection in audit logs for application systems. Inf. Syst. 44, 22–33 (2014)
Kanhere, P., Khanuja, H.: A survey on outlier detection in financial transactions. Int. J. Comput. Appl. 108(17), 23–25 (2014)
Han, J., Kamber, M., Pei, J.: Outlier Detection - Data Mining: Concepts and Techniques, 3rd edn. Elsevier (2012). ISBN 978-0-12-381479-1
Khanuja, H.K., Adane, D.S.: Forensic analysis for monitoring database transactions. In: Mauri, J.L., Thampi, S.M., Rawat, D.B., Jin, D. (eds.) SSCC 2014. CCIS, vol. 467, pp. 201–210. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44966-0_19
Adedayo, O.M., Olivier, M.S.: Ideal log setting for database forensics reconstruction. Digit. Invest. 12, 27–40 (2015). https://doi.org/10.1016/j.diin.2014.12.002. www.sciencedirect.com
Fowler, K.: SQL Server Forensic Analysis. Pearson Education, Addison-Wesley (2009). ISBN: 9780321533203
Litchfield, D.: Oracle Forensics Part 1: Dissecting the Redo Logs. NISR Publication (2007)
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Khanuja, H.K., Adane, D. (2019). Detection of Suspicious Transactions with Database Forensics and Theory of Evidence. In: Thampi, S., Madria, S., Wang, G., Rawat, D., Alcaraz Calero, J. (eds) Security in Computing and Communications. SSCC 2018. Communications in Computer and Information Science, vol 969. Springer, Singapore. https://doi.org/10.1007/978-981-13-5826-5_32
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DOI: https://doi.org/10.1007/978-981-13-5826-5_32
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