Algorithms for localisation of crosstalk attacks in AllOptical Network
The main aims of this study are divided into, firstly, a generic model of crosstalk attack and its monitoring with three levels of crosstalk attack propagation are determined. Secondly, the determined crosstalk attack and monitor model is investigated and a method which has the capability to detect...
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my-mmu-ep.63012016-01-28T03:46:59Z Algorithms for localisation of crosstalk attacks in AllOptical Network 2013-02 Siew, Hong Wei TK7800-8360 Electronics The main aims of this study are divided into, firstly, a generic model of crosstalk attack and its monitoring with three levels of crosstalk attack propagation are determined. Secondly, the determined crosstalk attack and monitor model is investigated and a method which has the capability to detect and locate multiple crosstalk attacks under sparse monitoring, where only a set of nodes is selected as monitor node in AON, is proposed subsequently. Two problems are addressed in this research study regards the crosstalk attack localisation under sparse monitoring in AON, namely the determination of placement of monitoring device in a network by the use of Greedy Monitor Placement Algorithm and the subsequent development of the identification and localisation algorithm of multiple crosstalk attack in AON. The use of sparse monitoring for crosstalk attack localisation in AON significantly reduces the cost of equipping all nodes with monitoring device. Lastly, to demonstrate the robustness of the proposed algorithms, a worstcase scenario where alltoall connections are assumed to contaminate with crosstalk in all the nodes in a network. The proposed algorithms are tested in several networks with different attacks. 2013-02 Thesis http://shdl.mmu.edu.my/6301/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php masters Multimedia University Faculty of Computing and Informatics |
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TK7800-8360 Electronics |
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TK7800-8360 Electronics Siew, Hong Wei Algorithms for localisation of crosstalk attacks in AllOptical Network |
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The main aims of this study are divided into, firstly, a generic model of crosstalk attack and its monitoring with three levels of crosstalk attack propagation are determined. Secondly, the determined crosstalk attack and monitor model is investigated and a method which has the capability to detect and locate multiple crosstalk attacks under sparse monitoring, where only a set of nodes is selected as monitor node in AON, is proposed subsequently. Two problems are addressed in this research study regards the crosstalk attack localisation under sparse monitoring in AON, namely the determination of placement of monitoring device in a network by the use of Greedy Monitor Placement Algorithm and the subsequent development of the identification and localisation algorithm of multiple crosstalk attack in AON. The use of sparse monitoring for crosstalk attack localisation in AON significantly reduces the cost of equipping all nodes with monitoring device. Lastly, to demonstrate the robustness of the proposed algorithms, a worstcase scenario where alltoall connections are assumed to contaminate with crosstalk in all the nodes in a network. The proposed algorithms are tested in several networks with different attacks. |
format |
Thesis |
qualification_level |
Master's degree |
author |
Siew, Hong Wei |
author_facet |
Siew, Hong Wei |
author_sort |
Siew, Hong Wei |
title |
Algorithms for localisation of crosstalk attacks in AllOptical Network |
title_short |
Algorithms for localisation of crosstalk attacks in AllOptical Network |
title_full |
Algorithms for localisation of crosstalk attacks in AllOptical Network |
title_fullStr |
Algorithms for localisation of crosstalk attacks in AllOptical Network |
title_full_unstemmed |
Algorithms for localisation of crosstalk attacks in AllOptical Network |
title_sort |
algorithms for localisation of crosstalk attacks in alloptical network |
granting_institution |
Multimedia University |
granting_department |
Faculty of Computing and Informatics |
publishDate |
2013 |
_version_ |
1747829625313034240 |