Protecting cyber physical systems using neural networks
The versatile, distributed, and heterogeneous nature of Cyber Physical Systems (CPSs) has made it integral to the fourth industry revolution. However, this has also made them prone to various cyber and/or physical security threats and attacks. Anomaly Detection is an effective solution to address...
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| Formato: | Final Year Project (FYP) |
| Idioma: | English |
| Publicado em: |
Nanyang Technological University
2022
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| Acesso em linha: | https://hdl.handle.net/10356/156663 |
| _version_ | 1826118484450344960 |
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| author | Koshy, Ajay Philip |
| author2 | Anupam Chattopadhyay |
| author_facet | Anupam Chattopadhyay Koshy, Ajay Philip |
| author_sort | Koshy, Ajay Philip |
| collection | NTU |
| description | The versatile, distributed, and heterogeneous nature of Cyber Physical
Systems (CPSs) has made it integral to the fourth industry revolution. However,
this has also made them prone to various cyber and/or physical security threats
and attacks. Anomaly Detection is an effective solution to address these
concerns, and one of the approaches involves the use of semi-supervised deep
neural networks. Deploying these networks closer to the edge increases privacy,
and reduces latency and network load. Therefore, the architectural design of
these models should be optimized to cater to the power consumption, memory,
and computational constraints of a microcontroller (MCU). This project studies
the performance of one-dimensional convolutional neural network (CNN)
models, designed for uni-variate time series prediction and anomaly detection,
while being constrained for embedding into edge devices. Multiple variants
of resource efficient convolutional networks were tested on the Secure Water
Treatment (SWaT) dataset. After which, they were compared for their time
series prediction accuracy, anomaly detection accuracy, and training time. |
| first_indexed | 2024-10-01T04:44:32Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156663 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:44:32Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1566632022-04-22T02:48:11Z Protecting cyber physical systems using neural networks Koshy, Ajay Philip Anupam Chattopadhyay Naina Gupta School of Computer Science and Engineering Naina Gupta anupam@ntu.edu.sg, Naina@ntu.edu.sg Engineering::Computer science and engineering The versatile, distributed, and heterogeneous nature of Cyber Physical Systems (CPSs) has made it integral to the fourth industry revolution. However, this has also made them prone to various cyber and/or physical security threats and attacks. Anomaly Detection is an effective solution to address these concerns, and one of the approaches involves the use of semi-supervised deep neural networks. Deploying these networks closer to the edge increases privacy, and reduces latency and network load. Therefore, the architectural design of these models should be optimized to cater to the power consumption, memory, and computational constraints of a microcontroller (MCU). This project studies the performance of one-dimensional convolutional neural network (CNN) models, designed for uni-variate time series prediction and anomaly detection, while being constrained for embedding into edge devices. Multiple variants of resource efficient convolutional networks were tested on the Secure Water Treatment (SWaT) dataset. After which, they were compared for their time series prediction accuracy, anomaly detection accuracy, and training time. Bachelor of Engineering (Computer Science) 2022-04-22T02:47:31Z 2022-04-22T02:47:31Z 2022 Final Year Project (FYP) Koshy, A. P. (2022). Protecting cyber physical systems using neural networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156663 https://hdl.handle.net/10356/156663 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Koshy, Ajay Philip Protecting cyber physical systems using neural networks |
| title | Protecting cyber physical systems using neural networks |
| title_full | Protecting cyber physical systems using neural networks |
| title_fullStr | Protecting cyber physical systems using neural networks |
| title_full_unstemmed | Protecting cyber physical systems using neural networks |
| title_short | Protecting cyber physical systems using neural networks |
| title_sort | protecting cyber physical systems using neural networks |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/156663 |
| work_keys_str_mv | AT koshyajayphilip protectingcyberphysicalsystemsusingneuralnetworks |