A Botnet Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent

Botnets must be combated in a concerted manner if they are not to become a danger to global security in the coming years. Botnet detection is currently performed at the host and/or network levels, but these options have important drawback which antivirus, firewalls and anti-spyware are not effective...

Full description

Saved in:
Bibliographic Details
Main Author: Ong, Wei Cheng
Format: Undergraduates Project Papers
Language:en
Published: 2023
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/40202/1/CA19098.pdf
http://umpir.ump.edu.my/id/eprint/40202/
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1831530027937693696
author Ong, Wei Cheng
author_facet Ong, Wei Cheng
author_sort Ong, Wei Cheng
building UMPSA Library
collection Institutional Repository
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
continent Asia
country Malaysia
description Botnets must be combated in a concerted manner if they are not to become a danger to global security in the coming years. Botnet detection is currently performed at the host and/or network levels, but these options have important drawback which antivirus, firewalls and anti-spyware are not effective against this threat because they are not able to detect hosts that are compromised via new or malicious software. Therefore, this paper will propose the method and develop a system to detect botnet malware. In order to detect the botnet malware, this study uses feature selection with product-moment correlation coefficient and trains it using decision tree classifier. The botnet detection system is developed according to the decision tree classifier.
format Undergraduates Project Papers
id my.ump.umpir.40202
institution Universiti Malaysia Pahang
language en
publishDate 2023
record_format eprints
spelling my.ump.umpir.402022024-02-07T04:24:45Z http://umpir.ump.edu.my/id/eprint/40202/ A Botnet Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent Ong, Wei Cheng QA75 Electronic computers. Computer science Botnets must be combated in a concerted manner if they are not to become a danger to global security in the coming years. Botnet detection is currently performed at the host and/or network levels, but these options have important drawback which antivirus, firewalls and anti-spyware are not effective against this threat because they are not able to detect hosts that are compromised via new or malicious software. Therefore, this paper will propose the method and develop a system to detect botnet malware. In order to detect the botnet malware, this study uses feature selection with product-moment correlation coefficient and trains it using decision tree classifier. The botnet detection system is developed according to the decision tree classifier. 2023-01 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40202/1/CA19098.pdf Ong, Wei Cheng (2023) A Botnet Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent. Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah.
spellingShingle QA75 Electronic computers. Computer science
Ong, Wei Cheng
A Botnet Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent
title A Botnet Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent
title_full A Botnet Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent
title_fullStr A Botnet Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent
title_full_unstemmed A Botnet Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent
title_short A Botnet Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent
title_sort botnet detection system with product moment correlation coefficient (pmcc) heatmap intelligent
topic QA75 Electronic computers. Computer science
url http://umpir.ump.edu.my/id/eprint/40202/1/CA19098.pdf
http://umpir.ump.edu.my/id/eprint/40202/
url_provider http://umpir.ump.edu.my/