Please use this identifier to cite or link to this item:
Title: Design and implementation on an anomaly detection scheme supported by neural networks
Authors: Chia, Maximillian Khim Heng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: A3158-191
Abstract: 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.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Design and Implementation of An Anomaly Detection Scheme Supported by Neural Networks Final Submission.pdf
  Restricted Access
581.54 kBAdobe PDFView/Open

Page view(s)

Updated on May 23, 2022


Updated on May 23, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.