Design and implementation on an anomaly detection scheme supported by neural networks
Smart grids have the potential to create a revolution in the energy industry. Smart grids have multiple benefits ranging from financial, to social and most importantly, sustainability by allowing for easier reduction of dependence on non-renewable energy sources. However, the operation of smart grid...
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sg-ntu-dr.10356-1403822023-07-07T18:51:55Z Design and implementation on an anomaly detection scheme supported by neural networks Chia, Maximillian Khim Heng Ma Maode School of Electrical and Electronic Engineering emdma@ntu.edu.sg Engineering::Electrical and electronic engineering Smart grids have the potential to create a revolution in the energy industry. Smart grids have multiple benefits ranging from financial, to social and most importantly, sustainability by allowing for easier reduction of dependence on non-renewable energy sources. However, the operation of smart grids are vastly different from the traditional grids. With the requirement of bi-directional communication links and increased reliance on information and communication technology, the smart grids are vulnerable to security threats. Moreover,as it has been demonstrated in the past that any security breach in cyber-physical systems, such as the smart grids, catering to the critical sectors like energy can have massive social, economic and technological impacts and can take the organisations decades to recover. The smart grid networks characteristics such as heterogeneity, delay constraints, bandwidth, scalability, and others make it challenging to deploy uniform security approaches all over the networks segments. One approach to provide a second line of defense for the smart grid networks. In this work, various cyber security requirements are analysed and security threats are reviewed. Based on the guidelines a scalable online intrusion detection system is designed to act as the second line of defence for the smart grid. The design is then attempted to be implemented on python using Tensorflow 2. There were flaws during implementation using NSL-KDD dataset, hence comparison with other relevant implementations could not be done. Other publications on implementation of the design in other fields were observed and a hypothesis was made based off the successes and failures of those works. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-28T08:20:14Z 2020-05-28T08:20:14Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140382 en A3158-191 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Chia, Maximillian Khim Heng Design and implementation on an anomaly detection scheme supported by neural networks |
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Smart grids have the potential to create a revolution in the energy industry. Smart grids have multiple benefits ranging from financial, to social and most importantly, sustainability by allowing for easier reduction of dependence on non-renewable energy sources. However, the operation of smart grids are vastly different from the traditional grids. With the requirement of bi-directional communication links and increased reliance on information and communication technology, the smart grids are vulnerable to security threats. Moreover,as it has been demonstrated in the past that any security breach in cyber-physical systems, such as the smart grids, catering to the critical sectors like energy can have massive social, economic and technological impacts and can take the organisations decades to recover. The smart grid networks characteristics such as heterogeneity, delay constraints, bandwidth, scalability, and others make it challenging to deploy uniform security approaches all over the networks segments. One approach to provide a second line of defense for the smart grid networks. In this work, various cyber security requirements are analysed and security threats are reviewed. Based on the guidelines a scalable online intrusion detection system is designed to act as the second line of defence for the smart grid. The design is then attempted to be implemented on python using Tensorflow 2. There were flaws during implementation using NSL-KDD dataset, hence comparison with other relevant implementations could not be done. Other publications on implementation of the design in other fields were observed and a hypothesis was made based off the successes and failures of those works. |
author2 |
Ma Maode |
author_facet |
Ma Maode Chia, Maximillian Khim Heng |
format |
Final Year Project |
author |
Chia, Maximillian Khim Heng |
author_sort |
Chia, Maximillian Khim Heng |
title |
Design and implementation on an anomaly detection scheme supported by neural networks |
title_short |
Design and implementation on an anomaly detection scheme supported by neural networks |
title_full |
Design and implementation on an anomaly detection scheme supported by neural networks |
title_fullStr |
Design and implementation on an anomaly detection scheme supported by neural networks |
title_full_unstemmed |
Design and implementation on an anomaly detection scheme supported by neural networks |
title_sort |
design and implementation on an anomaly detection scheme supported by neural networks |
publisher |
Nanyang Technological University |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/140382 |
_version_ |
1772826855527677952 |