Enhancement of system resilience under intentional attack

There are many types of networks that exist around us. One of the most commonly used networks are the scale-free networks. Example of such networks that are present around us include the Internet and social networks. However, scale-free networks are susceptible to intentional attacks. As scale-free...

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Main Author: Tan, Suan Wei
Other Authors: Xiao Gaoxi
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/78152
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-781522023-07-07T17:16:30Z Enhancement of system resilience under intentional attack Tan, Suan Wei Xiao Gaoxi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering There are many types of networks that exist around us. One of the most commonly used networks are the scale-free networks. Example of such networks that are present around us include the Internet and social networks. However, scale-free networks are susceptible to intentional attacks. As scale-free networks are prevalent in our daily lives, it is important to study the network and design methods to enhance the resilience of the scale-free network when it is under intentional attack. In this report, the impact of scale-free network under both intentional attack and random attack would be tested. Evaluations would be made after observing the impacts the simulation of attack have on the network. Thereafter, attachment of edges to the random nodes would be carried out. The results obtained would be used to evaluate if the scale-free network is successfully enhanced. Python would be used throughout this project to construct, simulate and evaluate the effects intentional attack on scale-free networks. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-12T08:49:49Z 2019-06-12T08:49:49Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78152 en Nanyang Technological University 39 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Tan, Suan Wei
Enhancement of system resilience under intentional attack
description There are many types of networks that exist around us. One of the most commonly used networks are the scale-free networks. Example of such networks that are present around us include the Internet and social networks. However, scale-free networks are susceptible to intentional attacks. As scale-free networks are prevalent in our daily lives, it is important to study the network and design methods to enhance the resilience of the scale-free network when it is under intentional attack. In this report, the impact of scale-free network under both intentional attack and random attack would be tested. Evaluations would be made after observing the impacts the simulation of attack have on the network. Thereafter, attachment of edges to the random nodes would be carried out. The results obtained would be used to evaluate if the scale-free network is successfully enhanced. Python would be used throughout this project to construct, simulate and evaluate the effects intentional attack on scale-free networks.
author2 Xiao Gaoxi
author_facet Xiao Gaoxi
Tan, Suan Wei
format Final Year Project
author Tan, Suan Wei
author_sort Tan, Suan Wei
title Enhancement of system resilience under intentional attack
title_short Enhancement of system resilience under intentional attack
title_full Enhancement of system resilience under intentional attack
title_fullStr Enhancement of system resilience under intentional attack
title_full_unstemmed Enhancement of system resilience under intentional attack
title_sort enhancement of system resilience under intentional attack
publishDate 2019
url http://hdl.handle.net/10356/78152
_version_ 1772826071958290432