Vulnerability assessment of complex networks based on optimal flow measurements under intentional node and edge attacks
Date Issued
2009-09-28
Author(s)
Kojchev, Risto
Kocarev, Ljupcho
Abstract
In this paper we assess the vulnerability of different synthetic
complex networks by measuring the traffic performance in presence of
intentional nodes and edge attacks. We choose which nodes or edges would be
attacked by using several centrality measures, such as: degree, eigenvector and
betweenness centrality. In order to obtain some information about the
vulnerability of the four different complex networks (random, small world,
scale-free and random geometric) we analyze the throughput of these networks
when the nodes or the edges are attacked using some of the above mentioned
strategies. When attack happens, the bandwidth is reallocated among the flows,
which affects the traffic utility. One of the obtained results shows that the scalefree network gives the best flow performance and then comes random networks,
small world, and the poorest performance is given by the random geometric
networks. This changes dramatically after removing some of the nodes (or
edges), giving the biggest performance drop to random and scale-free networks
and smallest to random geometric and small world networks.
complex networks by measuring the traffic performance in presence of
intentional nodes and edge attacks. We choose which nodes or edges would be
attacked by using several centrality measures, such as: degree, eigenvector and
betweenness centrality. In order to obtain some information about the
vulnerability of the four different complex networks (random, small world,
scale-free and random geometric) we analyze the throughput of these networks
when the nodes or the edges are attacked using some of the above mentioned
strategies. When attack happens, the bandwidth is reallocated among the flows,
which affects the traffic utility. One of the obtained results shows that the scalefree network gives the best flow performance and then comes random networks,
small world, and the poorest performance is given by the random geometric
networks. This changes dramatically after removing some of the nodes (or
edges), giving the biggest performance drop to random and scale-free networks
and smallest to random geometric and small world networks.
Subjects
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