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.
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Abstract
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.
| Item Type: | Undergraduates Project Papers |
|---|---|
| Additional Information: | SV: Dr. Ahmad Firdaus Bin Zainal Abidin |
| Uncontrolled Keywords: | botnet malware, decision tree classifier |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Mr. Nik Ahmad Nasyrun Nik Abd Malik |
| Date Deposited: | 07 Feb 2024 04:24 |
| Last Modified: | 07 Feb 2024 04:24 |
| URI: | http://umpir.ump.edu.my/id/eprint/40202 |
| Download Statistic: | View Download Statistics |
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