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An Anti-attack Model Based on Complex Network Theory in P2P networks

Published 14 years agoVersion 1arXiv:1108.5530

Authors

Hao Peng, Songnian Lu, Dandan Zhao, Aixin Zhang, Jianhua Li

Categories

cs.NIphysics.data-an

Abstract

Complex network theory is a useful way to study many real systems. In this paper, an anti-attack model based on complex network theory is introduced. The mechanism of this model is based on dynamic compensation process and reverse percolation process in P2P networks. The main purpose of the paper is: (i) a dynamic compensation process can turn an attacked P2P network into a power-law (PL) network with exponential cutoff; (ii) a local healing process can restore the maximum degree of peers in an attacked P2P network to a normal level; (iii) a restoring process based on reverse percolation theory connects the fragmentary peers of an attacked P2P network together into a giant connected component. In this way, the model based on complex network theory can be effectively utilized for anti-attack and protection purposes in P2P networks.

An Anti-attack Model Based on Complex Network Theory in P2P networks

14 years ago
v1
5 authors

Categories

cs.NIphysics.data-an

Abstract

Complex network theory is a useful way to study many real systems. In this paper, an anti-attack model based on complex network theory is introduced. The mechanism of this model is based on dynamic compensation process and reverse percolation process in P2P networks. The main purpose of the paper is: (i) a dynamic compensation process can turn an attacked P2P network into a power-law (PL) network with exponential cutoff; (ii) a local healing process can restore the maximum degree of peers in an attacked P2P network to a normal level; (iii) a restoring process based on reverse percolation theory connects the fragmentary peers of an attacked P2P network together into a giant connected component. In this way, the model based on complex network theory can be effectively utilized for anti-attack and protection purposes in P2P networks.

Authors

Hao Peng, Songnian Lu, Dandan Zhao et al. (+2 more)

arXiv ID: 1108.5530
Published Aug 29, 2011

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