By Topic

Network Immunization with Distributed Autonomy-Oriented Entities

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Chao Gao ; Beijing Key Lab. of Multimedia & Intell. Software, Beijing Univ. of Technol., Beijing, China ; Jiming Liu ; Ning Zhong

Many communication systems, e.g., internet, can be modeled as complex networks. For such networks, immunization strategies are necessary for preventing malicious attacks or viruses being percolated from a node to its neighboring nodes following their connectivities. In recent years, various immunization strategies have been proposed and demonstrated, most of which rest on the assumptions that the strategies can be executed in a centralized manner and/or that the complex network at hand is reasonably stable (its topology will not change overtime). In other words, it would be difficult to apply them in a decentralized network environment, as often found in the real world. In this paper, we propose a decentralized and scalable immunization strategy based on a self-organized computing approach called autonomy-oriented computing (AOC) [1], [2]. In this strategy, autonomous behavior-based entities are deployed in a decentralized network, and are capable of collectively finding those nodes with high degrees of conductivities (i.e., those that can readily spread viruses). Through experiments involving both synthetic and real-world networks, we demonstrate that this strategy can effectively and efficiently locate highly-connected nodes in decentralized complex network environments of various topologies, and it is also scalable in handling large-scale decentralized networks. We have compared our strategy with some of the well-known strategies, including acquaintance and covering strategies on both synthetic and real-world networks.

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:22 ,  Issue: 7 )