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ISG Research Seminar 24 October 2013

24/10/2013 (11:00-12:00)

Contact: Lorenzo Cavallaro


Speaker: Arman (MHR) Khouzani  (Royal Holloway University of London, UK)

Arman (MHR) Khouzani received the B.Sc. degree in EE from Sharif University of Technology, Tehran, Iran in 2006, and joined University of Pennsylvania (UPenn), School of Engineering and Applied Science (SEAS), Philadelphia, PA,  with fellowship award under supervision of Professor Saswati Sarkar. He received the M.S.E. and Ph.D. degrees in Electrical and Systems Engineering (ESE) from UPenn in 2008 and 2011, respectively. His PhD thesis received the 2012 Joseph and Rosaline Wolf Award of Best Male Dissertation in Electrical and Systems Engineering at UPenn. He then did postdoctoral research at Ohio State University and University of Southern California. Arman is currently a postdoctoral research assistant as part of the Information Security Group (ISG) at Royal Holloway, University of London in UK. His research interests are in game theory and optimal control in networks and information security.

Title: Optimal Control and Dynamic Games of Malware Epidemics, and more!


In this talk, I will present some application of  optimal control and dynamic game theory in the context of malware epidemics.

First, I will present a mean-field epidemic model that represents the spread of a contact-based malware. The model incorporates the dynamic decisions of both the system manager and the attacker. Next, I will present a general
mathematical framework for computing optimal controls of systems governed by nonlinear epidemic evolution using maximum principle in optimal control and differential games. Further, using some real analyses techniques, I will demonstrate how one can extract substantial information about the structure of optimum policies in the absence a closed-form solution. Specifically, I will argue how simple threshold-based policies prove to be optimal. 

Time allowing, I will then discuss my recent research at RHUL in the incentive analysis of outsourced computation using a principal-agent model from game theory. Feedback is welcome! 


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