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OWASP’s list have been changed since 2004 in terms of priorities; XSS and inject flaws are on the rise. Details can be found on OWASP’s website. 数据挖掘研究院
数据挖掘研究院
OWASP .NET Projects
http://www.owasp.org/index.php/Category:OWASP_.NET_Project
References and Papers on Financial Data Mining 数据挖掘交友
- Mine Your Way to Combat Money Laundering
- OFAC SDN List www.ustreas.gov/offices/enforcement/ofac/sdn/
- FinCen www.fincen.gov/
- FATF www.fatf-gafi.org/
- Suspicious Activity Report
- Keys to a Well Prepared Suspicious Activity Report
- A framework for data mining-based anti-money laundering research
- Profiling Behavior: The social construction of categories in the detection of financial crime; dissertation by Ana Canhoto
- Towards a Proactive Fraud Management Framework for Financial Data Streams
- T. Senator. "The financial crimes enforcement network AI system (FAIS)." AI Magazine 4, 1995.
- M. Sparrow. "The State of the Fraud Control Game; and the Impact of Electronic Claims Processing on Fraud and Fraud Control." Proceedings of the International Symposium on Criminal Justice Information Systems and Technology, 1994.
- U.S. Congress, Office of Technology Assessment (OTA). "Information Technologies for Control of Money Laundering." OTA-ITC-630. Washington, DC: U.S. Government Printing Office, September 1995.
- Zdanowicz, J.S. (2004), "Detecting money laundering and terrorist financing via data mining", Communications of the ACM, Vol. 47 No.5
- Watkins, R.C., Reynolds, K.M., Demara, R., Georgiopoulos, M., Gonzalez, A., Eaglin, R. (2003), "Tracking dirty proceeds: exploring data mining technologies as tools to investigate money laundering", Police Practice and Research, Vol. 4 No.2, pp.163-78.
- Vikram, A., Chennuru, S., Rao, H.R., Upadhyaya, S. (2004), "A solution architecture for financial institutions to handle illegal activities: a neural networks approach", Proceedings of the 37th Hawaii International Conference on System Sciences-2004
- Zhang, Z., Salerno, J.J., Yu, P.S. (2003), "Applying data mining in investigating money laundering crimes", paper presented at SIGKDD'03, Washington, DC, pp.747-52.
- Senator, T.E., Goldberg, H.G., Wooton, J. (1995), "The financial crimes enforcement network AI system (FAIS): identifying potential money laundering from reports of large cash transactions", AI Magazine, Vol. 16 No.4, pp.21-39.
- Tang, J., Yin, J. (2005), "Developing an intelligent data discriminating system of antimony laundering based on SVM", Proceedings of the Fourth International Conference on Machine Learning and Cybernetics. Guangzhou, pp.3453-7.
- Kingdon, J. (2004), "AI fights money laundering", IEEE Intelligent Systems, Vol. 5/6 pp.87
- Goldberg, H.G., Wong, R.W.H. (1998), "Restructuring transactional data for link analysis in the FinCEN AI System", Proceedings of 1998 AAAI Fall Symposium on Artificial Intelligence and Link Analysis, AAAI Press, Menlo Park, CA, .
- Fawcett, T., Provost, F. (1997), "Adaptive fraud detection", Data Mining and Knowledge Discovery, Vol. 1 No.3, pp.291-316.
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