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Malware Filtering for Network Security Using Weighted Optimality Michael Bloem, Tansu Alpcan, Stephan Schmidt, and Tamer Basar
 

Summary: Malware Filtering for Network Security Using Weighted Optimality
Measures
Michael Bloem, Tansu Alpcan, Stephan Schmidt, and Tamer Basžar
Coordinated Science Lab, University of Illinois, 1308 West Main Street, Urbana, IL 61801, USA
Email: {mbloem2, tbasar}@control.csl.uiuc.edu
Deutsche Telekom Laboratories, Technische Universitšat Berlin, Ernst-Reuter-Platz 7, 10587, Germany
Email: tansu.alpcan@telekom.de
DAI-Labor, Technische Universitšat Berlin, Franklinstr. 28, 10587, Germany
stephan.schmidt@dai-labor.de
Abstract-- We study the deployment and configuration of the
next generation of network traffic filters within a quantitative
framework. Graph-theoretic and optimization methods are
utilized to find optimal network traffic filtering strategies that
achieve various security or cost objectives subject to hardware
or security level constraints. We rely on graph-theoretic con-
cepts such as centrality measures to assess the importance of
individual routers within the network, given a traffic pattern.
In addition, we consider several possible objectives involving
financial costs associated with traffic filtering, the cost of failing
to filter traffic, a utility associated with filtering traffic, and

  

Source: Alpcan, Tansu - Deutsche Telekom Laboratories & Technische Universität Berlin

 

Collections: Engineering