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Beware the Soothsayer: From Attack Prediction Accuracy to Predictive Reliability in Security Games

  • Benjamin Ford
  • Thanh Nguyen
  • Milind Tambe
  • Nicole Sintov
  • Francesco Delle Fave
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9406)

Abstract

Interdicting the flow of illegal goods (such as drugs and ivory) is a major security concern for many countries. The massive scale of these networks, however, forces defenders to make judicious use of their limited resources. While existing solutions model this problem as a Network Security Game (NSG), they do not consider humans’ bounded rationality. Previous human behavior modeling works in Security Games, however, make use of large training datasets that are unrealistic in real-world situations; the ability to effectively test many models is constrained by the time-consuming and complex nature of field deployments. In addition, there is an implicit assumption in these works that a model’s prediction accuracy strongly correlates with the performance of its corresponding defender strategy (referred to as predictive reliability). If the assumption of predictive reliability does not hold, then this could lead to substantial losses for the defender. In the following paper, we (1) first demonstrate that predictive reliability is indeed strong for previous Stackelberg Security Game experiments. We also run our own set of human subject experiments in such a way that models are restricted to learning on dataset sizes representative of real-world constraints. In the analysis on that data, we demonstrate that (2) predictive reliability is extremely weak for NSGs. Following that discovery, however, we identify (3) key factors that influence predictive reliability results: the training set’s exposed attack surface and graph structure.

Keywords

Prediction Accuracy Source Node Target Node Predictive Reliability Maximum Absolute Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was supported by MURI Grant W911NF-11-1-0332 and by CREATE under grant number 2010-ST-061-RE0001.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Benjamin Ford
    • 1
  • Thanh Nguyen
    • 1
  • Milind Tambe
    • 1
  • Nicole Sintov
    • 1
  • Francesco Delle Fave
    • 1
  1. 1.University of Southern CaliforniaLos AngelesUSA

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