Deep learning for anomaly detection

Anomaly detection methods are devoted to target detection schemes in which no priori information about the spectra of the targets of interest is available. This paper research on the 4 various types of anomaly detection machine learning anomaly models, namely Local Outlier Factor (LOF), Isolation...

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Main Author:	Tan, Kenneth Jun Wei
Other Authors:	Wang Dan Wei
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2022
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/157429
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-157429
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spelling	sg-ntu-dr.10356-1574292023-07-07T19:14:22Z Deep learning for anomaly detection Tan, Kenneth Jun Wei Wang Dan Wei School of Electrical and Electronic Engineering EDWWANG@ntu.edu.sg Engineering::Electrical and electronic engineering Anomaly detection methods are devoted to target detection schemes in which no priori information about the spectra of the targets of interest is available. This paper research on the 4 various types of anomaly detection machine learning anomaly models, namely Local Outlier Factor (LOF), Isolation Forest, One-Class Support Vector Machine (SVM), and Robust Covariance. Additionally, this paper shows the various steps in the implementation anomaly models and studies the effectiveness of each model in analysing an industrialized Multivariate Time-Series dataset. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-15T05:06:19Z 2022-05-15T05:06:19Z 2022 Final Year Project (FYP) Tan, K. J. W. (2022). Deep learning for anomaly detection. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157429 https://hdl.handle.net/10356/157429 en A1144-211 application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering
spellingShingle	Engineering::Electrical and electronic engineering Tan, Kenneth Jun Wei Deep learning for anomaly detection
description	Anomaly detection methods are devoted to target detection schemes in which no priori information about the spectra of the targets of interest is available. This paper research on the 4 various types of anomaly detection machine learning anomaly models, namely Local Outlier Factor (LOF), Isolation Forest, One-Class Support Vector Machine (SVM), and Robust Covariance. Additionally, this paper shows the various steps in the implementation anomaly models and studies the effectiveness of each model in analysing an industrialized Multivariate Time-Series dataset.
author2	Wang Dan Wei
author_facet	Wang Dan Wei Tan, Kenneth Jun Wei
format	Final Year Project
author	Tan, Kenneth Jun Wei
author_sort	Tan, Kenneth Jun Wei
title	Deep learning for anomaly detection
title_short	Deep learning for anomaly detection
title_full	Deep learning for anomaly detection
title_fullStr	Deep learning for anomaly detection
title_full_unstemmed	Deep learning for anomaly detection
title_sort	deep learning for anomaly detection
publisher	Nanyang Technological University
publishDate	2022
url	https://hdl.handle.net/10356/157429
_version_	1772825166583169024

Deep learning for anomaly detection

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