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Evaluating Accuracy in Prudence Analysis for Cyber Security

  • Omaru Maruatona
  • Peter Vamplew
  • Richard Dazeley
  • Paul A. WattersEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

Conventional Knowledge-Based Systems (KBS) have no way of detecting or signalling when their knowledge is insufficient to handle a case. Consequently, these systems may produce an uninformed conclusion when presented with a case beyond their current knowledge (brittleness) which results in the KBS giving incorrect conclusions due to insufficient knowledge or ignorance on a specific case. Prudence Analysis (PA) has been shown to be a viable alternative to brittleness in Ripple Down Rules (RDR) knowledge bases. To date, there have been two approaches to Prudence; attribute-based and structural-based prudence. This paper introduces Integrated Prudence Analysis (IPA), a novel Prudence method formed by combining these methods.

Keywords

IPA Expert systems Prudence analysis 

References

  1. 1.
    Prayote, F.: Knowledge based anomaly detection. University of New South Wales, Ph.D. thesis (2007)Google Scholar
  2. 2.
    Dazeley, R., Kang, B.: Rated MCRDR: finding non-linear relationships between classifications in MCRDR. In: 3rd International Conference on Hybrid Intelligent Systems, pp. 499–508. IOS Press, Melbourne (2003)Google Scholar
  3. 3.
    Edwards, G., Kang, B., Preston, P., Compton, P.: Prudent expert systems with credentials: managing the expertise of decision support systems. Int. J. Bio-Med. Comput. 40, 125–132 (1995)CrossRefGoogle Scholar
  4. 4.
    Richards, D.: Two decades of ripple down rules research. Knowl. Eng. Rev. 24(2), 159–184 (2009)CrossRefGoogle Scholar
  5. 5.
    Compton, P., Jansen, R.: Knowledge in context: a strategy for expert system maintenance. In: Barter, C.J., Brooks, M.J. (eds.) AI 1988. LNCS, vol. 406, pp. 292–306. Springer, Heidelberg (1990). doi: 10.1007/3-540-52062-7_86 Google Scholar
  6. 6.
    Kang, B., Compton, P., Preston, P.: Multiple classification ripple down rules: evaluation and possibilities. In: 9th Banff Knowledge Acquisition for Knowledge Based Systems Workshop, Banff, pp. 17–26 (1995)Google Scholar
  7. 7.
    Maruatona, O., Vamplew, P., Dazeley, R.: RM and RDM, a preliminary evaluation of two prudent RDR techniques. In: Richards, D., Kang, B.H. (eds.) PKAW 2012. LNCS, vol. 7457, pp. 188–194. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-32541-0_16 CrossRefGoogle Scholar
  8. 8.
    Compton, P., Cao, T.M.: Evaluation of incremental knowledge acquisition with simulated experts. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS, vol. 4304, pp. 39–48. Springer, Heidelberg (2006). doi: 10.1007/11941439_8 CrossRefGoogle Scholar
  9. 9.
    Prayote, A., Compton, P.: Detecting anomalies and intruders. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS, vol. 4304, pp. 1084–1088. Springer, Heidelberg (2006). doi: 10.1007/11941439_127 CrossRefGoogle Scholar
  10. 10.
    Metz, C.E.: Basic principles of ROC analysis. In: Seminars in Nuclear Medicine, pp. 283–298 (1978)Google Scholar
  11. 11.
    Phua, C., Lee, V., Smith, K., Gayler, R.: A comprehensive survey of data mining-based fraud detection research. Artif. Intell. Rev. (2005)Google Scholar
  12. 12.
    Layton, R., Watters, P.A., Dazeley, R.: Automatically determining phishing campaigns using the USCAP methodology. In: Proceedings of the 5th APWG E-crime Research Summit (2010)Google Scholar
  13. 13.
    Alazab, M., Venkatraman, S., Watters, P.A., Alazab, M.: Zero-day malware detection based on supervised learning algorithms of API call signatures. In: Proceedings of the 9th Australian Data Mining Conference (2011)Google Scholar
  14. 14.
    Amin, A., Anwar, S., Shah, B., Khattak, A.M.: Compromised user credentials detection using temporal features: a prudent based approach. In: Proceedings of the 9th International Conference on Computer and Automation Engineering, pp. 104–110 (2017)Google Scholar
  15. 15.
    Haq, I.U., Gondal, I., Vamplew, P., Layton, R.: Generating Synthetic Datasets for Experimental Validation of Fraud Detection (2016)Google Scholar
  16. 16.
    Finlayson, A., Compton, P.: Run-time validation of knowledge-based systems. In: Proceedings of the Seventh International Conference on Knowledge Capture, pp. 25–32 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Omaru Maruatona
    • 1
  • Peter Vamplew
    • 2
  • Richard Dazeley
    • 2
  • Paul A. Watters
    • 3
    Email author
  1. 1.PwCMelbourneAustralia
  2. 2.Federation UniversityBallaratAustralia
  3. 3.La Trobe UniversityMelbourneAustralia

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