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Comprehensive Review of One-Class Classification Approaches for Anomaly Detection

by Divya Challa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 45
Year of Publication: 2024
Authors: Divya Challa
10.5120/ijca2024924134

Divya Challa . Comprehensive Review of One-Class Classification Approaches for Anomaly Detection. International Journal of Computer Applications. 186, 45 ( Oct 2024), 69-74. DOI=10.5120/ijca2024924134

@article{ 10.5120/ijca2024924134,
author = { Divya Challa },
title = { Comprehensive Review of One-Class Classification Approaches for Anomaly Detection },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2024 },
volume = { 186 },
number = { 45 },
month = { Oct },
year = { 2024 },
issn = { 0975-8887 },
pages = { 69-74 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number45/comprehensive-review-of-one-class-classification-approaches-for-anomaly-detection/ },
doi = { 10.5120/ijca2024924134 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-10-31T00:31:02.471051+05:30
%A Divya Challa
%T Comprehensive Review of One-Class Classification Approaches for Anomaly Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 45
%P 69-74
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Anomaly detection is a crucial task in various domains, including cybersecurity, healthcare, and finance, where identifying rare and abnormal events is of paramount importance. One-Class Classification (OCC) methods have emerged as a powerful approach for this task, effectively distinguishing between normal and anomalous data when only the normal class is well-represented. This paper provides a comprehensive review of OCC techniques, categorizing them into four main approaches: Reconstruction-Based Methods, Variational Autoencoders (VAEs), Convolutional Approaches, and Hybrid Models. Additionally, it delves into the diverse applications of OCC, including the detection of rare diseases in healthcare, financial fraud prevention, and industrial fault detection. The review also addresses key challenges such as data imbalance, model interpretability, and scalability, while highlighting recent trends and advancements in the field.

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Index Terms

Computer Science
Information Sciences

Keywords

One-class classifcation (OCC) Anomaly Detection Variational Autoencoders (VEs) Convolutional Neural Networks (CNN) Outlier detection Convolutional Autoencoders (CAE) Generative Adversarial Networks (GANs) Long Short-Term Memory (LSTM) Principal components analysis (PCA) Self-Organizing Map (SOM) One-Class Support Vector Machine (OCSVM) Deep learning.

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