A data-driven method for network anomaly attack detection in public transport system

With the popularity of the Internet, more and more businesses and transactions rely on the network to complete, and at the same time, anomaly attacks against various network applications are becoming more and more frequent. How to detect and identify various network anomalies has become an unavoidab...

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Main Author: Yin, Rui
Other Authors: Goh Wang Ling
Format: Theses and Dissertations
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/78416
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-784162023-07-04T16:20:41Z A data-driven method for network anomaly attack detection in public transport system Yin, Rui Goh Wang Ling School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems With the popularity of the Internet, more and more businesses and transactions rely on the network to complete, and at the same time, anomaly attacks against various network applications are becoming more and more frequent. How to detect and identify various network anomalies has become an unavoidable technical issue. The network attack methods emerge in an endless stream, and the attack methods are constantly updated, making the traditional security mechanisms such as firewalls difficult to detect for many attacks. As an effective defense technology, intrusion detection technology makes up for the shortcomings of traditional security technology and has been concerned by researchers at home and abroad. With the continuous expansion of network scale, the continuous growth of network traffic and the continuous development of hacker technologies, higher requirements are placed on the performance of network anomaly detection. This thesis designs an anomaly attack detection based on self-organizing mapping algorithm to improve the accuracy of anomaly detection technology. Through the unsupervised machine learning method, the daily data of the transportation system is clustered and a training model is established. Finally, abnormal attack detection is performed according to the clustering model. Master of Science (Electronics) 2019-06-19T13:33:26Z 2019-06-19T13:33:26Z 2019 Thesis http://hdl.handle.net/10356/78416 en 90 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Yin, Rui
A data-driven method for network anomaly attack detection in public transport system
description With the popularity of the Internet, more and more businesses and transactions rely on the network to complete, and at the same time, anomaly attacks against various network applications are becoming more and more frequent. How to detect and identify various network anomalies has become an unavoidable technical issue. The network attack methods emerge in an endless stream, and the attack methods are constantly updated, making the traditional security mechanisms such as firewalls difficult to detect for many attacks. As an effective defense technology, intrusion detection technology makes up for the shortcomings of traditional security technology and has been concerned by researchers at home and abroad. With the continuous expansion of network scale, the continuous growth of network traffic and the continuous development of hacker technologies, higher requirements are placed on the performance of network anomaly detection. This thesis designs an anomaly attack detection based on self-organizing mapping algorithm to improve the accuracy of anomaly detection technology. Through the unsupervised machine learning method, the daily data of the transportation system is clustered and a training model is established. Finally, abnormal attack detection is performed according to the clustering model.
author2 Goh Wang Ling
author_facet Goh Wang Ling
Yin, Rui
format Theses and Dissertations
author Yin, Rui
author_sort Yin, Rui
title A data-driven method for network anomaly attack detection in public transport system
title_short A data-driven method for network anomaly attack detection in public transport system
title_full A data-driven method for network anomaly attack detection in public transport system
title_fullStr A data-driven method for network anomaly attack detection in public transport system
title_full_unstemmed A data-driven method for network anomaly attack detection in public transport system
title_sort data-driven method for network anomaly attack detection in public transport system
publishDate 2019
url http://hdl.handle.net/10356/78416
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