TY - GEN
T1 - Optimal and robust epidemic response for multiple networks
AU - Bloem, Michael
AU - Alpcan, Tansu
AU - Başar, Tamer
N1 - Funding Information:
The authors would like to thank the Boeing Corporation and Deutsche Telekom, AG for their support of this research, for the former through the Information Trust Institute at the University of Illinois at Urbana-Champaign. An earlier, more concise version of this paper was presented at the 46th IEEE Conference on Decision and Control, New Orleans, December 12–14, 2007, with the same title.
PY - 2007
Y1 - 2007
N2 - We study the optimization of malicious software removal or patch deployment processes across multiple networks. The well-known classical epidemic model is adapted to model malware propagation in this multi-network framework. We capture the trade-off between the infection spread and the patching costs in a cost function, leading to an optimal control problem. We linearize the system to derive feedback controllers using pole-placement, linear quadratic regulator (LQR) optimal control, and H∞ optimal control, where we explicitly model measurement errors in the number of infected clients. The resulting patching strategies are analyzed numerically and their results are compared. The proportional response that is typically assumed for the classical epidemic model is shown to be sub-optimal.
AB - We study the optimization of malicious software removal or patch deployment processes across multiple networks. The well-known classical epidemic model is adapted to model malware propagation in this multi-network framework. We capture the trade-off between the infection spread and the patching costs in a cost function, leading to an optimal control problem. We linearize the system to derive feedback controllers using pole-placement, linear quadratic regulator (LQR) optimal control, and H∞ optimal control, where we explicitly model measurement errors in the number of infected clients. The resulting patching strategies are analyzed numerically and their results are compared. The proportional response that is typically assumed for the classical epidemic model is shown to be sub-optimal.
UR - http://www.scopus.com/inward/record.url?scp=62749102764&partnerID=8YFLogxK
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U2 - 10.1109/CDC.2007.4434524
DO - 10.1109/CDC.2007.4434524
M3 - Conference contribution
AN - SCOPUS:62749102764
SN - 1424414989
SN - 9781424414987
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 5074
EP - 5079
BT - Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 46th IEEE Conference on Decision and Control 2007, CDC
Y2 - 12 December 2007 through 14 December 2007
ER -