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Alvanof, Mulia
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Computer Science & IT

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Penerapan Algoritma Random Forest dalam Deteksi dan Klasifikasi Ransomware Alvanof, Mulia; Bustami; Rozzi Kesuma Dinata
Jurnal Elektronika dan Teknologi Informasi Vol 5 No 2 (2024): September 2024
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5201/jet.v5i2.488

Abstract

Ransomware is a type of malware that blocks access to computer systems or data until a ransom is paid by the victim. Ransomware attacks typically occur due to malicious files that are unknowingly downloaded and installed by the victim onto their computer system. Given the threats and potential losses posed, methods for detecting and classifying ransomware continue to be developed, one of which utilizes the Random Forest machine learning algorithm. Random Forest is chosen for its advantages in handling large datasets, short training time, high prediction accuracy, and its ability to reduce the risk of overfitting. Using 1380 ransomware samples from a dataset with 54 features, 10 best features were selected through Feature Selection where the built Random Forest model successfully predicted ransomware files with an accuracy of 98.79%.
Penerapan Algoritma Random Forest dalam Deteksi dan Klasifikasi Ransomware Alvanof, Mulia; Bustami; Rozzi Kesuma Dinata
Jurnal Elektronika dan Teknologi Informasi Vol 5 No 2 (2024): September 2024
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5201/jet.v5i2.488

Abstract

Ransomware is a type of malware that blocks access to computer systems or data until a ransom is paid by the victim. Ransomware attacks typically occur due to malicious files that are unknowingly downloaded and installed by the victim onto their computer system. Given the threats and potential losses posed, methods for detecting and classifying ransomware continue to be developed, one of which utilizes the Random Forest machine learning algorithm. Random Forest is chosen for its advantages in handling large datasets, short training time, high prediction accuracy, and its ability to reduce the risk of overfitting. Using 1380 ransomware samples from a dataset with 54 features, 10 best features were selected through Feature Selection where the built Random Forest model successfully predicted ransomware files with an accuracy of 98.79%.
Co-Authors Bustami Rozzi Kesuma Dinata