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Title: Deep learning for anomaly detection
Authors: Tan, Kenneth Jun Wei
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Tan, K. J. W. (2022). Deep learning for anomaly detection. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: A1144-211
Abstract: 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.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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