Securing industry IoT systems with cyber security and fault diagnosis approaches

Securing industry IoT systems with cyber security and fault diagnosis approaches

In this dissertation, the fault diagnosis approaches based on machine learning algorithms are discussed. For industrial processes, faults may be caused by a network attack. In this project, the data packets on the internet will be captured and processed to extract some useful information. Back propa...

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Main Author: Lyu, Yuansen
Other Authors: Goh Wang Ling
Format: Theses and Dissertations
Language:English
Published: 2019
Subjects:
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Online Access:http://hdl.handle.net/10356/78624
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-78624
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spelling sg-ntu-dr.10356-786242023-07-04T16:20:14Z Securing industry IoT systems with cyber security and fault diagnosis approaches Lyu, Yuansen Goh Wang Ling School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence In this dissertation, the fault diagnosis approaches based on machine learning algorithms are discussed. For industrial processes, faults may be caused by a network attack. In this project, the data packets on the internet will be captured and processed to extract some useful information. Back propagation network and support vector machine are the most popular machine learning algorithms, which have many advantages such as quick and efficient. In the project, lots of historical network data will be trained by the above two algorithms and tested to obtain an optimal fault diagnosis approach. Master of Science (Electronics) 2019-06-24T12:49:38Z 2019-06-24T12:49:38Z 2019 Thesis http://hdl.handle.net/10356/78624 en 58 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::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Lyu, Yuansen
Securing industry IoT systems with cyber security and fault diagnosis approaches
description In this dissertation, the fault diagnosis approaches based on machine learning algorithms are discussed. For industrial processes, faults may be caused by a network attack. In this project, the data packets on the internet will be captured and processed to extract some useful information. Back propagation network and support vector machine are the most popular machine learning algorithms, which have many advantages such as quick and efficient. In the project, lots of historical network data will be trained by the above two algorithms and tested to obtain an optimal fault diagnosis approach.
author2 Goh Wang Ling
author_facet Goh Wang Ling
Lyu, Yuansen
format Theses and Dissertations
author Lyu, Yuansen
author_sort Lyu, Yuansen
title Securing industry IoT systems with cyber security and fault diagnosis approaches
title_short Securing industry IoT systems with cyber security and fault diagnosis approaches
title_full Securing industry IoT systems with cyber security and fault diagnosis approaches
title_fullStr Securing industry IoT systems with cyber security and fault diagnosis approaches
title_full_unstemmed Securing industry IoT systems with cyber security and fault diagnosis approaches
title_sort securing industry iot systems with cyber security and fault diagnosis approaches
publishDate 2019
url http://hdl.handle.net/10356/78624
_version_ 1772825916835102720

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