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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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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2019
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| Online Access: | http://hdl.handle.net/10356/78624 |
| _version_ | 1826114299467137024 |
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| author | Lyu, Yuansen |
| author2 | Goh Wang Ling |
| author_facet | Goh Wang Ling Lyu, Yuansen |
| author_sort | Lyu, Yuansen |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T03:37:12Z |
| format | Thesis |
| id | ntu-10356/78624 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:37:12Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Lyu, Yuansen Securing industry IoT systems with cyber security and fault diagnosis approaches |
| title | 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_short | Securing industry IoT systems with cyber security and fault diagnosis approaches |
| title_sort | securing industry iot systems with cyber security and fault diagnosis approaches |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | http://hdl.handle.net/10356/78624 |
| work_keys_str_mv | AT lyuyuansen securingindustryiotsystemswithcybersecurityandfaultdiagnosisapproaches |