A Framework for Anomaly Detection under Dynamic and Distributed Scenarios

About this item

Title

A Framework for Anomaly Detection under Dynamic and Distributed Scenarios

Content partner

Victoria University of Wellington

Collection

Open Access Victoria University of Wellington

Description

<b>Anomaly Detection is an important aspect of many application domains. It refers to the problem of finding patterns in data that do not conform to expected behaviour. Hence, understanding of expected behaviour well is fundamental to performing effective anomaly detection. However, data profiles constantly evolve in certain domains such as computer networks. In other domains such as traffic monitoring and healthcare, data are distributed and are either too large or there are privacy concerns...

Read more

Format

Research paper

Research format

Scholarly text / Thesis

Thesis level

Doctoral

Date created

2021

Creator

Murugaraj Odiathevar

URL

https://figshare.com/articles/thesis/A_Framework_for_Anomaly_Detection_under_Dynamic_and_Distribu...

Related subjects

Other engineering not elsewhere classified / anomaly detection / online / offline / machine learning / bayesian / dynamic / distributed / School: School of Engineering and Computer Science / Engineering not elsewhere classified / Degree Discipline: Engineering / Degree Level: Doctoral / Degree Name: Doctor of Philosophy

What is the copyright status of this item?

Share

See below for specifics about how you may use this item.

More Information

Victoria University of Wellington has this to say about the rights status of this item:

CC BY-NC-ND 4.0

What can I do with this item?

You must always check with Victoria University of Wellington to confirm the specific terms of use, but this is our understanding:

Non-infringing use

NZ Copyright law does not prevent every use of a copyright work. You should consider what you can and cannot do with a copyright work.

Share it

This item is suitable for copying and sharing with others, without further permission.

No modifying

You are not allowed to adapt or remix this item into any other works.

No commercial use

You may not use this item commercially.