Ransomware Detection on Linux Using Machine Learning with Random Forest Algorithm

Abstract

Ransomware continues to pose a significant threat to cybersecurity, particularly affecting critical systems running on Linux. The novel application of the random forest algorithm for detecting ransomware on Linux systems offers a significant advancement, leveraging machine learning to enhance detection accuracy and adaptability. The methodology involved collecting a diverse dataset of ransomware samples and benign files, followed by meticulous feature extraction and the application of a robust random forest model. Performance evaluation demonstrated high precision, recall, and overall accuracy, surpassing existing detection methods such as support vector machines and neural networks. Comparative analysis highlighted the model's superior ability to handle high-dimensional data and manage complex feature interactions, resulting in more reliable and accurate detection. Despite the computational complexity and extensive preprocessing requirements, the findings underscore the model's potential to significantly improve cybersecurity measures against ransomware. The comprehensive evaluation provides valuable insights into the development of more effective detection mechanisms, affirming the random forest algorithm's pivotal role in mitigating ransomware threats on Linux systems.

Topics & Concepts