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Cybersecurity Anomaly Detection using Power BI

Rare events deviate from the majority of regular patterns in a dataset. These events can include any unanticipated behaviour, fraud, intrusion, or suspected aberrant event that may be harmful or useful to the domain application that is unidentified with a large volume of data. These are known as ano...

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Main Author: Fairuz, Muhammad Irfan
Format: Final Year Project
Language:English
Published: 2022
Subjects:
T Technology (General)
Online Access:http://utpedia.utp.edu.my/id/eprint/24524/1/Cybersecurity%20Anomaly%20Detection%20using%20Power%20BI.pdf
http://utpedia.utp.edu.my/id/eprint/24524/
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spelling oai:utpedia.utp.edu.my:245242023-05-18T06:59:53Z http://utpedia.utp.edu.my/id/eprint/24524/ Cybersecurity Anomaly Detection using Power BI Fairuz, Muhammad Irfan T Technology (General) Rare events deviate from the majority of regular patterns in a dataset. These events can include any unanticipated behaviour, fraud, intrusion, or suspected aberrant event that may be harmful or useful to the domain application that is unidentified with a large volume of data. These are known as anomalies, and they must be detected since they could be any form of network attack, a sudden drop/increase in sales, the spread of illness, or terrorist activities. 2022-09 Final Year Project NonPeerReviewed text en http://utpedia.utp.edu.my/id/eprint/24524/1/Cybersecurity%20Anomaly%20Detection%20using%20Power%20BI.pdf Fairuz, Muhammad Irfan (2022) Cybersecurity Anomaly Detection using Power BI. [Final Year Project] (Submitted)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Fairuz, Muhammad Irfan
Cybersecurity Anomaly Detection using Power BI
description Rare events deviate from the majority of regular patterns in a dataset. These events can include any unanticipated behaviour, fraud, intrusion, or suspected aberrant event that may be harmful or useful to the domain application that is unidentified with a large volume of data. These are known as anomalies, and they must be detected since they could be any form of network attack, a sudden drop/increase in sales, the spread of illness, or terrorist activities.
format Final Year Project
author Fairuz, Muhammad Irfan
author_facet Fairuz, Muhammad Irfan
author_sort Fairuz, Muhammad Irfan
title Cybersecurity Anomaly Detection using Power BI
title_short Cybersecurity Anomaly Detection using Power BI
title_full Cybersecurity Anomaly Detection using Power BI
title_fullStr Cybersecurity Anomaly Detection using Power BI
title_full_unstemmed Cybersecurity Anomaly Detection using Power BI
title_sort cybersecurity anomaly detection using power bi
publishDate 2022
url http://utpedia.utp.edu.my/id/eprint/24524/1/Cybersecurity%20Anomaly%20Detection%20using%20Power%20BI.pdf
http://utpedia.utp.edu.my/id/eprint/24524/
_version_ 1768010136686690304
score 13.211869

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