TY - JOUR
T1 - A note on the spread of worms in scale-free networks
AU - Griffin, Christopher
AU - Brooks, Richard
N1 - Funding Information:
Manuscript received August 5, 2004; revised January 7, 2005 and April 5, 2005. This work was supported by the Office of Naval Research under Award N00014-01-1-0859. This paper was recommended by Associate Editor E. Santos. C. Griffin is with the Applied Research Laboratory, Penn State University, State College, PA 16804-0030 USA (e-mail: [email protected]). R. Brooks is with the Computer Science Department, Clemson University, Clemson, SC 29634 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TSMCB.2005.854498
PY - 2006/2
Y1 - 2006/2
N2 - This paper considers the spread of worms in computer networks using insights from epidemiology and percolation theory. We provide three new results. The first result refines previous work showing that epidemics occur in scale-free graphs more easily because of their structure. We argue, using recent results from random graph theory that for scaling factors between 0 and ∼3.4875, any computer worm infection of a scale-free network will become an epidemic. Our second result uses this insight to provide a mathematical explanation for the empirical results of Chen and Carley, who demonstrate that the Countermeasure Competing strategy can be more effective for immunizing networks to viruses or worms than traditional approaches. Our third result uses random graph theory to contradict the current supposition that, for very large networks, monocultures are necessarily more susceptible than diverse networks to worm infections.
AB - This paper considers the spread of worms in computer networks using insights from epidemiology and percolation theory. We provide three new results. The first result refines previous work showing that epidemics occur in scale-free graphs more easily because of their structure. We argue, using recent results from random graph theory that for scaling factors between 0 and ∼3.4875, any computer worm infection of a scale-free network will become an epidemic. Our second result uses this insight to provide a mathematical explanation for the empirical results of Chen and Carley, who demonstrate that the Countermeasure Competing strategy can be more effective for immunizing networks to viruses or worms than traditional approaches. Our third result uses random graph theory to contradict the current supposition that, for very large networks, monocultures are necessarily more susceptible than diverse networks to worm infections.
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U2 - 10.1109/TSMCB.2005.854498
DO - 10.1109/TSMCB.2005.854498
M3 - Article
C2 - 16468578
AN - SCOPUS:31844435712
SN - 1083-4419
VL - 36
SP - 198
EP - 202
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 1
ER -