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...
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| Main Author: | |
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40202/1/CA19098.pdf http://umpir.ump.edu.my/id/eprint/40202/ |
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| Summary: | 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. |
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