ACE Seminar: Bridging large-scale data collection and analysis

Speaker: Nicolas Christin

Date/Time: 28-Sep-2017, 16:00 UTC

Venue: Roberts G08 - Sir David Davis LT

Details

Abstract

In this talk, I will argue that understanding incentives of both attackers and targets has become critical to strengthening online security. I will advocate the need for an interdisciplinary research agenda, ranging from network measurements and large-scale data analysis to human factor modeling. Using case studies (online sale of unlicensed pharmaceutical drugs, and anonymous marketplaces), I will first describe how longitudinal, large-scale measurements and data analysis reveal important economic and structural properties of a priori complex criminal ecosystems. I will then discuss how these structural properties can be used to design successful interventions, both from a policy and from a technical angle. On the policy side, I will show that our criminal ecosystem analysis evidences "concentration points," whose disruption could effectively hamper illicit operations. On the technical side, I will demonstrate how we can use adversaries' incentives to design and build systems that can proactively identify future attack targets. I will conclude by outlining a roadmap for security research combining measurements, mathematical modeling and behavioral aspects.

Bio

Nicolas Christin is an Associate Research Professor at Carnegie Mellon University, jointly appointed in the School of Computer Science and in Engineering & Public Policy. His research interests are in computer and information systems security; most of his work is at the boundary of systems and policy research. He has most recently focused on security analytics, online crime modeling, and economics and human aspects of computer security. His group's research won several awards including Honorable Mention at ACM CHI 2011 and 2016, Best Student Paper Award at USENIX Security 2014, and Best Paper Award at USENIX Security 2016 and ACM CHI 2017. He equally enjoys field measurements and mathematical modeling.

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