KLASIFIKASI PDF MALWARE PADA GARBA RUJUKAN DIGITAL (GARUDA) KEMDIKBUD DIKTI DENGAN METODE LOGISTIC REGRESSION

LESTARI, VIRGINITA PUTRI and Stiawan, Deris and Afifah, Nurul (2024) KLASIFIKASI PDF MALWARE PADA GARBA RUJUKAN DIGITAL (GARUDA) KEMDIKBUD DIKTI DENGAN METODE LOGISTIC REGRESSION. Undergraduate thesis, Sriwijaya University.

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Abstract

Malware that can enter through PDF files that appear unsuspicious is one of the main factors in cyber security attacks. The GARUDA dataset was analyzed statically using VirusTotal and PDFiD to identify whether a PDF file is dangerous or not, then classification was carried out to determine the characteristics of the PDF file using the Logistic Regression method of the Multinomial type. The dataset used consists of 10,000 PDF format files with 21 prediction variables and there are 3 class categories. The GARUDA dataset has unbalanced data, therefore a Random Oversampling technique is used to overcome it. The results show that the Multinomial Logistic Regression model is able to achieve an accuracy of 93%. These results indicate that the model has reliable performance in performing classification.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Logistic Regression, PDF Malware, Imbalance Dataset
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.A25 Computer security. Systems and Data Security.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Virginita Putri Lestari
Date Deposited:	24 Jun 2024 07:36
Last Modified:	24 Jun 2024 07:36
URI:	http://repository.unsri.ac.id/id/eprint/147784

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