KLASIFIKASI MALWARE BERDASARKAN FITUR API CALL DAN ANDROID PERMISSIONS MENGGUNAKAN RADIAL BASIS FUNCTION - Bagas Aditya Wibisono (2022)
Malware has become a major threat to technology users today. Malware or Malicious Software is intrusive software with the purpose of infecting, browsing, stealing, or damaging a device. Various malware detection methods have been developed to anticipate the development of malware. This study focuses on malware classification based on API calls and Android permissions using Radial Basis Function Network with K-Means Clustering as the centroid selection method. Radial Basis Function Network is part of an artificial neural network that uses a Gaussian function as its activation function, while K-Means Clustering is an unsupervised learning algorithm in machine learning or clustering algorithms. The dataset used is malgenome-215-dataset which can be downloaded from the figshare repository. Data split is done with K-Fold. The tests were carried out based on the hyperparameters of the learning rate, the number of epochs, the number of hidden units, and the number of K in the K-Fold. Accuracy, precision, recall, and F1 scores were calculated based on the confusion matrix. The experimental results showed 98.41% accuracy, 99.3% precision, 97.92% recall, and 98.6% F1 score.