Ransomware attacks on Android devices have been increasing in recent years, posing a signif- icant threat to users’ data and privacy. This research presents a comprehensive evaluation of four popular machine learning algorithms – KNN, NN, Random Forest (RF), and SVM – in detecting Android ransomware. The study leverages a publicly available dataset from Kaggle, containing ten different types of ransomware attacks and benign instances of Android applica- tions, extracting relevant features for analysis. The performance of each classifier is assessed using various evaluation metrics, including accuracy, precision, recall, and F1-score. The results demonstrate that the RF classifier achieves the highest accuracy of 96.22%, followed by SVM with an accuracy of 83.51%, NN at 81.91%, and finally KNN at 70.49%. Furthermore, the research explores the strengths and limitations of each algorithm, providing insights into their suitability for real-world ransomware detection scenarios. The findings contribute to the devel- opment of robust and efficient security mechanisms for safeguarding Android devices against the evolving threat of ransomware.
Evaluating machine learning algorithms in detecting Android ransomware
Momposhi, L. (Author). 2024
Western Sydney University thesis: Master's thesis