Skip to main content
SpringerLink
Log in

Managing the false alarms: A framework for assurance and verification of surveillance monitoring

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

This article discusses methods to support assurance of surveillance monitoring and compliance verification knowledge management (CV-KM). The discussion includes aspects of primary monitoring systems, the different environments in which they operate, the verification problem solving and decision making tasks, the problem structure, and the coordination of the review process to facilitate truth maintenance and regulatory Meta rules. Based on the ALCOD (Alert Coding) prototype developed with the Surveillance Division of the Australian Stock Exchange (ASX), the surveillance operation is considered a primary monitoring function with the analysis of the resulting output the second-tier monitoring function—the assurance component.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abdi, H. (2007). Signal detection theory. In N. J. Salkind (Ed.), Encyclopedia of measurement and statistics (pp. 886–889) Thousand Oaks (CA): Sage.

    Google Scholar 

  • Abdul-gader, A., & Koza, K. (1990). Discourse analysis for knowledge acquisition: The coherence method. Journal of Management Information Systems, 6(4), 61–81.

    Google Scholar 

  • ASX (2006). Retrieved from http://www.asx.com.au/supervision/enforcing_rules/enforcement_unit.htm.

  • Australian Financial Review (2006). ASIC plans $30m project to target insider trading. Monday, 11, December 11, 2006, 1,14.

  • Berry, J., & Yanco, G. (1990). Enter the ASX computer police. Journal of the Securities Institute of Australia, 1, 2–5, March.

    Google Scholar 

  • Blocher, E., & Cooper, J. (1988). A study of auditors’ analytical review performance. Auditing: A Journal of Practice and Theory, 1–28, (Spring).

  • Bond, A. H., & Gasser, L. (Eds.) (1988). Readings in Distributed Artificial Intelligence. San Mateo: Morgan Kaufman.

  • Boose, J. H. (1985). A knowledge acquisition program or expert systems based on personal psychology construct. International Journal of Man-Machine Studies, 23(5), 495–525.

    Google Scholar 

  • Buchanan, J. H. (1982). Expertise transfer for expert system design. New York: Elsevier.

    Google Scholar 

  • Busby, J., & Strutt, J. (2001). What limits the ability of design organizations to predict failure? Proceedings of the Institution of Mechanical Engineers (215) Part B: 1471–1474.

  • Butler, K. A., & Corter, J. E. (1986). Use of psychometric tools for knowledge acquisition. In W. A. Gale (ed.), Artificial Intelligence in Statistics. Reading, Addison-Wesley, MA, 295–319.

  • Byrnes, E. C., Thomas, N. H., & Waldman, S. (1990). Inspector: An expert system for monitoring world-wide trading activities in foreign exchange. AI Review, (3), 9–13.

  • Chang, A., Bailey, A. Jr., & Whinston, A. (1993). Multi-auditor cooperation: A model of distributed reasoning. IEEE Transactions On Engineering Management, 20(4), 346–359.

    Article  Google Scholar 

  • Clarke, R. (1988). Information technology and dataveillance. Communications of the ACM, 31(5), 498–512.

    Article  Google Scholar 

  • Cohen, J. (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213–220.

    Article  Google Scholar 

  • Cravetto, C., Lesmo, L., Molino, G., & Torasso, P. (1985). LITO2: A frame based expert system for medical diagnosis in hepatology. In I. De Lotto, M. Stefanelli (eds.), Artificial intelligence in medicine (pp. 107–119) Amsterdam: North-Holland.

    Google Scholar 

  • Davis, S., & Ord, K. (1990). Improving and measuring the performance of a security industry surveillance system. Interfaces, 20(5), 31–42.

    Article  Google Scholar 

  • Debowski, S. (2006). Knowledge management. Sydney: Wiley and Sons.

    Google Scholar 

  • de Kleer, J. (1986). An assumption based truth maintenance system. Artificial Intelligence, 28(2), 127–162.

    Article  Google Scholar 

  • Diederich, J., Ruhmann, I., & May, M. (1987). Kriton: A knowledge acquisition tool for expert systems. International Journal of Man-Machine Studies, 26(1), 29–40.

    Article  Google Scholar 

  • Denning, D. (2006). Dorothy Denning’s home page. Retrieved from http://www.cs.georgetown.edu/~denning/index.html.

  • FBI. (1999). Digital storm. FBI Annual Report. Retrieved www.fbi.gov/programs/lab/labannual99.pdf.

  • Fleiss, J. L. (1981). Statistical methods for rates and proportions. Wiley: NY.

    Google Scholar 

  • Freedman, R. (1991). AI on wall street. IEEE Expert, 6(2), 2–7.

    Article  Google Scholar 

  • Freedman, R. S., & Mathai, J. (1995). Market analysis for risk management and regulation: An artificial intelligence approach. In R. Freedman, R. Klein, & J. Lederman (eds.), Artificial intelligence in the capital market. (pp. 315–326) Chicago, Illinois: Probus.

    Google Scholar 

  • Freedman, R. S., & Stuzin, G. J. (1991). Knowledge-based methodology for tuning analytical models. IEEE Transactions on Systems, Man, and Cybernetics, 21(2), 347–358.

    Article  Google Scholar 

  • Goldschmidt, P. (1996). Compliance monitoring for anomaly detection in a complex environment using multiple agents: Supporting capital market surveillance. Ph.D. dissertation, The University of Western Australia, July 1996. This was awarded The 1997 International Outstanding Doctoral Dissertation Award: Information Systems section of the American Accounting Association eligible for Ph.D. dissertations completed between the years 1994–1996 inclusive.

  • Goldschmidt, P. (2000). CMAD and integrated safeguards. Invited presentation to The Australian Department of Foreign Affairs and Trade, Australian Safeguards and Non-proliferation Office, Canberra, Australia.

  • Goldschmidt, P. (2001). Assurance and compliance monitoring support. In G. Dhillon (Ed.), Information security management: Global challenges in the next millennium 10, (pp. 135–154). Hershey, PA: Idea Group.

  • Goldschmidt, P. (2005). Supporting assurance and compliance monitoring. In M. Khosrow-Pour (ed.), Encyclopedia of information science and technology, 5, 2684–2689, Hershey, PA: Information Resources Management Association, Idea Group.

  • Goldschmidt, P. (2006). Compliance monitoring for anomaly detection. http://www.patentstorm.us/patents/6983266-description.html. US Patent Issued on January 2006.

  • Green, D. M., & Swet, J. A. (1966). Signal detection theory and psychophysics. New York: Wiley.

    Google Scholar 

  • Hayes-Roth, F., Waterman, D. A., & Lenat, D. B. (1983). Building expert systems. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Holsapple, C. W., & Whinston, A. B. (1987). Knowledge-based organisations. The Information Society, 5(2), 77–90.

    Article  Google Scholar 

  • Howorka, G. V., Anderson, L. A., Goul, K. M., & Hine, M. (1995). A computational model of coordination for the design of organisational decision support systems. Intelligent Systems in Accounting, Finance and Management, 4(1), 43–70.

    Google Scholar 

  • Jacob, V., & Pirkul, H. (1992). A framework for supporting distributed group decision-making. Decision Support Systems, 8(1), 17–28.

    Article  Google Scholar 

  • Jennex, M. E., & Olfman, L. (2006). A model of knowledge management success. International Journal of Knowledge Management, 3(2), 51–68.

    Google Scholar 

  • Jennex, M. E., Smolnik, S., & Croasdell, D. (2007). Towards defining knowledge management success, 40th Hawaii Conference on System Sciences, HICSS, IEEE Computer Society.

  • Jin, Y., & Levitt, R. E. (1993). i-Agents: Modeling organisational problem solving in multi-agent teams. Intelligent Systems in Accounting, Finance and Management, 2(4), 247–270.

    Google Scholar 

  • Koonce, L. (1993). A cognitive characterisation of audit analytical review. Auditing: A Journal of Practice and Theory, 12(Supplementary), 57–76.

    Google Scholar 

  • Landon, D., Munson, M., Nadeau, S., Landsman, S., Schroeder, B., Cheung, E. t al. (2006). Integrated stream processing and persistent data retrieval. Sensor Data Analysis Framework Research Roadmap, Mitre Corp, Virginia.

  • Libby, R. (1985). Availability and the generation of hypotheses in analytical review. Journal of Accounting Research, 23(2), 648–667.

    Article  Google Scholar 

  • Libby, R., & Trotman, K. (1993). The review process as a control for differential recall of evidence in auditor judgments. Accounting, Organisations and Society, 18(6), 559–574.

    Article  Google Scholar 

  • Liou, Y. (1990). Knowledge acquisition: Issues, techniques and methodologies. Data Base, 23(1), 59–64.

    Google Scholar 

  • Major, J., & Riedinger, D. (1992). EFD: A hybrid knowledge / statistical-based system for the detection of fraud. International Journal of Intelligent Systems, 7, 687–703.

    Article  Google Scholar 

  • Morrison, J. (1993). Team memory: Information support for business teams. Proceedings of the 26th Annual Hawaii International Conference on Systems Sciences, Wailea, Hawaii, 3, 122–131.

  • O’Keefe, R. M., & O’Leary, D. (1993). Expert systems verification and validation: A survey and tutorial. Artificial Intelligence Review, 7(1), 3–42.

    Article  Google Scholar 

  • O’Leary, D. (1991). Artificial intelligence and expert systems in accounting databases: Survey and extensions. Expert Systems with Applications, 3, 143–152.

    Article  Google Scholar 

  • Olsen, J., & Reuter, H. (1987). Extracting expertise from experts: Methods for knowledge acquisition. Expert Systems, 4(3), 152–168.

    Article  Google Scholar 

  • Pete, A., Kleinman, D., & Pattipati, K. (1993). Tasks and organisational signal detection model of organisational decision making. Intelligent Systems in Accounting, Finance and Management, 2(4), 289–303.

    Google Scholar 

  • Pieters, R., & Zeelenberg, M. (2005). On bad decisions and deciding badly: When intention-behavior inconsistency is regrettable. Journal of Organizational Behavior and Human Decision Processes, 97(1), 18–30.

    Article  Google Scholar 

  • Roohani, S. (2003). Trust and data assurances in capital markets: The role of technology solutions. Price Waterhouse Coopers research monograph, S J. Roohani, Ed. RI: Bryant College.

  • Senator, T., Goldberg, H., Wooten, J., Cottini, M., Khan, A., Klinger, C. et al. (1995). Financial crimes enforcement network AI system (FAIS). AI Magazine, 16(4), 21–39.

    Google Scholar 

  • Shaw, M. J., & Fox, M. S. (1993). Distributed artificial intelligence for group decision support. Decision Support Systems, 9(4), 349–367.

    Article  Google Scholar 

  • Simon, H. (1973). The structure of ill structured problems. Artificial Intelligence, 4(3–4), 181–201.

    Article  Google Scholar 

  • Simon, H. (1977). The new science of management decisions, revised edition. Engelwood Cliffs, NJ: Prentice Hall.

  • Sol, H. (1982). Simulation in Information Systems Development. Ph.D. dissertation, University of Groningen, The Netherlands.

  • SRI International. (1999). Experience with Emerald to Date. Retrieved from http://www.csl.sri.com/%7eneumann/det99.html.

  • Starbuck, W., & Milliken, F. (1988). Challenger: Fine-tuning the odds until something breaks. Journal of Management Studies, 25(4), 319–340.

    Article  Google Scholar 

  • Torasso, P., & Console, L. (1989). Diagnostic problem solving. London: North Oxford Academic.

    Google Scholar 

  • Vasarhelyi, M. A., & Halper, F. B. (1991). The continuous audit of online systems. Auditing: A Journal of Theory and Practice, 10(1), 110–125.

    Google Scholar 

  • Warkentin, M. (1993). Verbal domain knowledge acquisition: Unplugging the bottleneck. Heuristics: The Journal of Knowledge Engineering and Technology, 6(3), 21–26.

    Google Scholar 

  • Weaver, E., & Richardson, G. (2006). Threshold setting and the cycling of a decision threshold. System Dynamics Review, 2(1), 1–26.

    Article  Google Scholar 

  • Winston, P. (1977). Artificial intelligence. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Zadeh, L. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1, 3–28.

    Article  Google Scholar 

  • Zadeh, L. (1983). The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems, 11, 199–227.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. UWA Business School M261, The University of Western Australia, Crawley, WA 6907, Australia

    Peter Goldschmidt

Authors
  1. Peter Goldschmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter Goldschmidt.

Additional information

Alert-KM Pty Ltd. holds exclusive intellectual property rights to the CV-KM and CMAD business process methods. This IP is covered by full patents for Australian 758491, Singapore, 200106150-6, USA, 6,983,266, patent pending Hong Kong 02106820.1; Canada 2,366,548, EU Patent Cooperative Treaty International Application No. PCT AU00/00295 (19 countries).Aspects of this paper were presented at the 2nd Information Warfare and Security Conference, Perth 2001; the Information Systems Audit and Control Association Conference, Auckland, New Zealand, November 2004; the Encyclopaedia of Information Science and Technology (Five-Volume Set), Idea Group, Hershey, 2005; and the 3rd Australian Information Security Management Conference, at the Forensic Conference held at Edith Cowan University, September 2005, Perth Australia.“Compliance Monitoring for Anomaly Detection”—CMAD—coined by P. Goldschmidt in 1995 has no relationship or affiliation with “Computer Misuse and Anomaly Detection” previously coined by UC Davis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Goldschmidt, P. Managing the false alarms: A framework for assurance and verification of surveillance monitoring. Inf Syst Front 9, 541–556 (2007). https://doi.org/10.1007/s10796-007-9048-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-007-9048-1

Keywords

Search

Navigation