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Security and Communication Networks
Article . 2011 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
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DBLP
Article . 2011
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Fighting botnets with economic uncertainty

Authors: Zhen Li 0025; Qi Liao 0002; Andrew Blaich; Aaron Striegel;

Fighting botnets with economic uncertainty

Abstract

AbstractBotnets have become an increasing security concern in today's Internet. Since current technological defenses against botnets have failed to produce results, it has become necessary to think about different strategies. Given that money is perhaps the single determining force driving the growth in botnet attacks, we propose an interesting economic approach to take away the root cause of botnet, i.e., the financial incentives. In this paper, we model botnet‐related cyber crimes as a result of profit‐maximizing decision‐making optimization problem from the perspective of botmasters. By introducing theuncertaintylevel created by thevirtual bots, we make determining the optimal botnet size infeasible for the botnet operators, and consequently the botnet profitability can fall dramatically. The theoretical model presented here has a large potential to fight off botnet‐related attacks of varying revenue patterns. Copyright © 2010 John Wiley & Sons, Ltd.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Top 10%
Average
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