Penerapan Algoritma Random Forest dalam Deteksi dan Klasifikasi Ransomware
DOI:
https://doi.org/10.5201/jet.v5i2.488Keywords:
Ransomware, Machine Learning, Seleksi Fitur, Random ForestAbstract
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%.
References
V., Rameshbabu., C., Vijayakumaran., P., B., EDWIN, PRABHAKAR. (2023). Machine Learning. Character Lab tips, doi: 10.53776/tips-gratitude-machine-learning.
Dinata, R., Akbar, H., & Hasdyna, N. (2020). Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus. ILKOM Jurnal Ilmiah, 12(2), 104-111. doi:https://doi.org/10.33096/ilkom.v12i2.539.104-111.
Noorbehbahani, Fakhroddin & Rasouli, Farzaneh & Saberi, Mohammad. (2019). Analysis of Machine learning Techniques for Ransomware Detection. 128-133. 10.1109/ISCISC48546.2019.8985139.
Adamu, Umaru & Awan, Irfan. (2019). Ransomware Prediction Using Supervised Learning Algorithms. 57-63. 10.1109/FiCloud.2019.00016.
Dinata, R. K., Fajriana, F., Zulfa, Z., & Hasdyna, N. (2020). Klasifikasi Sekolah Menengah Pertama/Sederajat Wilayah Bireuen Menggunakan Algoritma K-Nearest Neighbors Berbasis Web. CESS (Journal of Computer Engineering, System and Science), 5(1), 33-37.
Bahaa, Yamany., Mahmoud, Said, Elsayed., Anca, Delia, Jurcut., Nashwa, Abdelbaki., Marianne, A., Azer. (2022). A New Scheme for Ransomware Classification and Clustering Using Static Features. Electronics, doi: 10.3390/electronics11203307.
Manabu, Hirano., Ryotaro, Kobayashi. (2022). Machine learning-based Ransomware Detection Using Low-level Memory Access Patterns Obtained From Live-forensic Hypervisor. doi: 10.1109/CSR54599.2022.9850340.
Masum, Mohammad & Hossain Faruk, Md Jobair & Shahriar, Hossain & Qian, Kai & Lo, Dan & Adnan, Muhaiminul. (2022). Ransomware Classification and Detection With Machine learning Algorithms. 10.1109/CCWC54503.2022.9720869.
Dinata, R. K., Hasdyna, N., & Alif, M. (2021). Applied of Information Gain Algorithm for Culinary Recommendation System in Lhokseumawe. Journal Of Informatics And Telecommunication Engineering, 5(1), 45-52.
Gustavo, Sosa-Cabrera., M., Garc'ia-Torres., Christian, E., Schaerer. (2023). Feature Selection: A perspective on inter-attribute cooperation. arXiv.org, doi: 10.48550/arXiv.2306.16559.
[11] Muliadi, Muliadi., Andi, Farmadi., Rudy, Herteno., Rahmat, Ramadhani. (2023). Random forest Dengan Random Search Terhadap Ketidakseimbangan Kelas Pada Prediksi Gagal Jantung. Jurnal Informatika, doi: 10.31294/inf.v10i1.14531
Rizal, R., Bustami, B., & Azzahra, D. (2019). Pendeteksi Tajwid Idgham Mutajanisain Pada Citra Al-Qur’an Menggunakan Fuzzy Associative Memory (FAM). TECHSI-Jurnal Teknik Informatika, 11(3), 395-407.
Dinata, R. K., Hasdyna, N., Retno, S., & Nurfahmi, M. (2021). K-means algorithm for clustering system of plant seeds specialization areas in east Aceh. ILKOM Jurnal Ilmiah, 13(3), 235-243.
M. Mathur, "Ransomware (malware) detection using Machine learning," GitHub, https://github.com/muditmathur2020/RansomwareDetection/blob/master/Ransomware.csv. [Diakses: Maret, 2024].
