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Adversarial Knowledge Discovery
November/December 2009 (vol. 24 no. 6)
pp. 54-61
David B. Skillicorn, Queen's University Kingston, Canada

In adversarial settings, knowledge discovery must be dynamic, adapting to both the changing face of normality and the rapidly changing properties of adversaries.

1. A.P. Dempster, N.M. Laird, and D.B. Rubin, "Maximum Likelihood from Incomplete Data via the EM Algorithm," J. Royal Statistical Soc., series B, vol. 39, no. 1, 1977, pp. 1–38.
2. J.G. Dutrisac and D.B. Skillicorn, "Hiding Clusters in Adversarial Settings," IEEE Int'l Conf. Intelligence and Security Informatics (ISI 08), IEEE Press, 2008, pp. 185–187.
3. C. Bishop, Neural Networks for Pattern Recognition, Oxford Univ. Press, 1995.
4. C.J.C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, 1998, pp. 121–167.
5. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge Univ. Press, 2000.
6. L. Breiman, Random Forests–Random Features, tech. report 567, Dept. of Statistics, Univ. of California, Berkeley, 1999.
7. J. Jonas and J. Harper, "Effective Counterterrorism and the Limited Role of Predictive Data Mining," Policy Analysis,11 Dec. 2006, pp. 1–12.
8. B. Schneier, "Why Data Mining Won't Stop Terror," Wired, 9 Mar. 2006,
9. J.G. Dutrisac and D.B. Skillicorn, "Subverting Prediction in Adversarial Settings," IEEE Int'l Conf. Intelligence and Security Informatics (ISI 08), IEEE Press, 2008, pp. 19–24.
10. D.P. Biros et al., "A Quasi-Experiment to Determine the Impact of a Computer Based Deception Detection Training System: The Use of Agent 99 Trainer in the US Military," Proc. 38th Hawaii Int'l Conf. Systems Science (HICSS 05) vol. 1, IEEE CS Press, 2005, p. 24.1.
11. M.L. Newman et al., "Lying Words: Predicting Deception from Linguistic Style," Personality and Social Psychology Bull., vol. 29, no. 5, 2003, pp. 665–675.
12. L. Zhou et al., "An Exploratory Study into Deception Detection in Text-Based Computer Mediated Communication," Proc. 36th Hawaii Int'l Conf. Systems Science (HICSS 03), IEEE CS Press, 2003, p. 44.2.

Index Terms:
data mining, law enforcement, fraud, counterterrorism, fringe clusters, predicting normality
David B. Skillicorn, "Adversarial Knowledge Discovery," IEEE Intelligent Systems, vol. 24, no. 6, pp. 54-61, Nov.-Dec. 2009, doi:10.1109/MIS.2009.108
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