Abstract
Since organizations cannot prevent all criminal activities of employees by security technology in practice, the application of IT forensic methods for finding traces in data is extremely important. However, new attack variants for occupational crime require new forensic tools and specific environments may require adoptions of methods and tools. Obviously, the development of tools or their adaption require testing using data containing corresponding traces of attacks. Since real-world data are often not available synthetic data are necessary to perform testing. With 3LSPG we propose a systematic method to generate synthetic test data which contain traces of selected attacks. These data can then be used to evaluate the performance of different forensic tools.
This work was supported by CASED ( www.cased.de ).
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References
Behrends, E.: Introduction to Markov Chains. Vieweg Verlag (2000)
Bolton, R., Hand, D.: Statistical Fraud Detection: A Review. Statistical Science 17(3) (2002)
Dacier, M., Deswarte, Y., Kaaniche, M.: Models and tools for quantitative assessment of operational security. In: IFIP SEC 1996 (1996)
Dacier, M., Deswarte, Y., Kaaniche, M.: Quantitative Assessment of Operational Security: Models and Tools. LAAS Research Report 96493 (May 1996)
Deloitte: Ten things about financial statement fraud — A review of SEC enforcement releases, 2000-2006 (June 2007), www.deloitte.com/us/forensiccenter
Germany’s Federal Criminal Police Office (BKA): Wirtschaftskriminalität — Bundeslagebild (2008), http://www.bka.de/lageberichte/wi.html
Jans, M., Lybaert, N., Vanhoff, K.: Data Mining for Fraud Detection: Toward an Improvement on Internal Control Systems? In: 30rd European Accounting Association, Ann. Congr., Lisbon (2007)
Jeske, D., Samadi, B., Lin, P., Ye, L., Cox, S., Xiao, R., Younglove, T., Ly, M., Holt, D., Rich, R.: Generation of synthetic data sets for evaluating the accuracy of knowledge discovery systems. In: ACM KDD 2005 (2005)
Kou, Y., Lu, C., Sirwongwattana, S., Huang, Y.: Survey of fraud detection techniques. In: IEEE Int. Conf. on Networking, Sensing & Control (2004)
KPMG: Anti Fraud Management — Best Practice der Prävention gegen Wirtschaftskriminalität. White Paper (2006)
KPMG: Profile of a Fraudster. White Paper (2007)
Lin, P., Samadi, B., Cipolone, A., Jeske, D., Cox, S., Rendon, C., Holt, D., Xiao, R.: Development of a synthetic data set generator for building and testing information discovery systems. In: 3rd Int. Conf. on Inf. Techn.: New Generations (2006)
Lundin, E., Kvarnström, H., Jonsson, E.: A synthetic fraud data generation methodology. In: Deng, R.H., Qing, S., Bao, F., Zhou, J. (eds.) ICICS 2002. LNCS, vol. 2513, pp. 265–277. Springer, Heidelberg (2002)
Lundin Barse, E., Kvarnström, H., Jonsson, E.: Synthesizing test data for fraud detection systems. In: Omondi, A.R., Sedukhin, S.G. (eds.) ACSAC 2003. LNCS, vol. 2823, Springer, Heidelberg (2003)
Nestler, C., Salvenmoser, S., Bussmann, K., Werle, M., Krieg, O.: Wirtschaftskriminalität 2007 — Sicherheitslage der deutschen Wirtschaft (2007), http://www.pwc.de/de/crimesurvey
Phua, C., Lee, V., Smith, K., Gayler, R.: A comprehensive survey of data mining-based fraud detection research. Working Paper (2005) (unpublished)
PriceWaterhouseCoopers: Key elements of antifraud programs and controls. White Paper (2003)
Richard, G., Roussev, V.: Next-Generation Digital Forensics. Communications of the ACMÂ 49(2) (2006)
Shamshad, A., Wan Hussin, W., Bawadi, M., Sanusi, S.: First and second order markov chain models for synthetic generation of wind speed time series. Energy 30(5) (2005)
Wells, J.: Corporate Fraud Handbook, 2nd edn. Wiley, Chichester (2007)
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Yannikos, Y., Franke, F., Winter, C., Schneider, M. (2011). 3LSPG: Forensic Tool Evaluation by Three Layer Stochastic Process-Based Generation of Data. In: Sako, H., Franke, K.Y., Saitoh, S. (eds) Computational Forensics. IWCF 2010. Lecture Notes in Computer Science, vol 6540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19376-7_18
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DOI: https://doi.org/10.1007/978-3-642-19376-7_18
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