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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Bibliographic Details
Main Author: Chia, Maximillian Khim Heng
Other Authors: Ma Maode
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/140382
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.