MALWARE DETECTION USING HONEYPOT AND MACHINE LEARNING
Malware is one of the main threats to the security of computer users. Malware has impacts that can disrupt and harm users, computers or networks. Some of the techniques in detecting malware consist of static analysis, dynamic analysis and machine learning. Machine learning has the ability to b...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/53886 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Malware is one of the main threats to the security of computer users. Malware has
impacts that can disrupt and harm users, computers or networks. Some of the
techniques in detecting malware consist of static analysis, dynamic analysis and
machine learning. Machine learning has the ability to be quite effective and
efficient compared to other techniques. In its application, machine learning has
limitations when it uses very little training data. Some malware datasets are not
updated and therefore have the potential to increase false positives when machine
learning tries to detect new malware. Therefore, a solution is needed that can
update the existing dataset. Updates to the dataset can be done by added the latest
malware information. A honeypot is a system that has the ability to detect and
collect new malware. In this study, a detection technique for malware using a
honeypot and machine learning was designed. The author combines the ClaMP
dataset with the feature extraction results on the honeypot. Then the authors built
a model using machine learning. At the end of this study the authors succeeded in
increasing the accuracy by 0.14% with the achievement of 99.37% and a recall of
0.06% with the achievement of 99.73%.
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