David Zage - Sandia National Laboratories

Sandia National Laboratories

Sep 14, 2011

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"What does Knowledge Discovery, Predictability, and Human Behavior have to do with Computer Security"

Abstract

Vast resources are devoted to predicting human behavior in domains
such as economics, popular culture, and national security, but the
quality of such predictions is often poor. Thus, it is tempting to
conclude that this inability to make good predictions is a consequence
of some fundamental lack of predictability on the part of humans.
However, recent work offers evidence that the failure of standard
prediction methods does not indicate an absence of human
predictability but instead reflects:
1. misunderstandings regarding which features of human dynamics
actually possess predictive power
2. the fact that, until recently, it has not been possible to measure
these predictive features in real world settings.
This talk introduces some of the science behind these basic
observations and demonstrates their utility in various case studies.
We begin by considering social groups in which individuals are
influenced by the behavior of others. Correctly identify and
understanding the social forces in these situations can increase the
extent to which the outcome of a social process can be predicted in
its very early stages. This finding is then leveraged to design
prediction methods which outperform existing techniques for predicting
social network dynamics. We also look at the analysis of the
predictability of adversary behavior in the co-evolutionary "arms
races" that exist between attackers and defenders in many domains. Our
analysis reveals that conventional wisdom regarding these co-evolving
systems is incomplete, and provides insights which enable the
development of predictive methods for computer network security.

About the Speaker

David Zage is a senior member of Sandia National Laboratories in the
Cyber Analysis R&D group. His main research interest are in the areas
of security, networking, and distributed systems. David received his
Ph.D. in computer science from Purdue University in 2010 and his B.S.
in computer science from Purdue in 2004.

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