TY - GEN
T1 - Improving resiliency of network topology with enhanced evolving strategies
AU - Kim, Soo
AU - Lee, Heejo
AU - Lee, Wan Yeon
PY - 2006
Y1 - 2006
N2 - Recent studies have shown that many real networks follow the power-law distribution of node degrees. Instead of random connectivity, however, power-law connectivity suffers from the vulnerability of targeted attacks, since Its Interconnection Is heavily relying on a very few nodes. In addition, the connectivity of power-law networks becomes more concentrated on the small group of nodes as time goes by, which can be explained by Barabasl and Albert's rich-get-richer model. The rich-get-richer model Is known as the most widely accepted generative model and follows the rule of preferential attachment to high-degree nodes. Thus, the preference of high-degree nodes to connect a newly created node renders the network less resilient as evolves. In this paper, we propose three different evolving strategies which can be applicable to the Internet topologies and the resiliency of evolving networks are measured by two resiliency metrics. From the experiments, we show that choosing an appropriate evolving strategy Is more effective to Increase the resiliency of network topology, rather than simply adding more links. Also, we show the possibility of Improving the attack resiliency of Internet topology by adapting only a part of networks, e.g. 20-40%, to a new evolving strategy, such as change from the maxdegree preference to the average-degree preference, which can be considered as a practical range of deployment.
AB - Recent studies have shown that many real networks follow the power-law distribution of node degrees. Instead of random connectivity, however, power-law connectivity suffers from the vulnerability of targeted attacks, since Its Interconnection Is heavily relying on a very few nodes. In addition, the connectivity of power-law networks becomes more concentrated on the small group of nodes as time goes by, which can be explained by Barabasl and Albert's rich-get-richer model. The rich-get-richer model Is known as the most widely accepted generative model and follows the rule of preferential attachment to high-degree nodes. Thus, the preference of high-degree nodes to connect a newly created node renders the network less resilient as evolves. In this paper, we propose three different evolving strategies which can be applicable to the Internet topologies and the resiliency of evolving networks are measured by two resiliency metrics. From the experiments, we show that choosing an appropriate evolving strategy Is more effective to Increase the resiliency of network topology, rather than simply adding more links. Also, we show the possibility of Improving the attack resiliency of Internet topology by adapting only a part of networks, e.g. 20-40%, to a new evolving strategy, such as change from the maxdegree preference to the average-degree preference, which can be considered as a practical range of deployment.
KW - Attack resiliency
KW - Evolving strategy
KW - Network topology
KW - Power-law distribution
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U2 - 10.1109/CIT.2006.102
DO - 10.1109/CIT.2006.102
M3 - Conference contribution
AN - SCOPUS:34547368987
SN - 076952687X
SN - 9780769526874
T3 - Proceedings - Sixth IEEE International Conference on Computer and Information Technology, CIT 2006
SP - 149
BT - Proceedings - Sixth IEEE International Conference on Computer and Information Technology, CIT 2006
PB - IEEE Computer Society
T2 - 6th IEEE International Conference on Computer and Information Technology, CIT 2006
Y2 - 20 September 2006 through 22 September 2006
ER -