Dynamic Feature Dataset for Ransomware Detection Using Machine Learning Algorithms.

ABSTRACT: Ransomware-related cyber-attacks have been on the rise over the last decade, disturbing organizations considerably. Developing new and better ways to detect this type of malware is necessary. This research applies dynamic analysis and machine learning to identify the ever-evolving ransomware signatures using selected dynamic features. Since most of the attributes are shared by diverse ransomware-affected samples, our study can be used for detecting current and even new variants of the threat. This research has the following objectives: (1) Execute experiments with encryptor and locker ransomware combined with goodware to generate JSON files with dynamic parameters using a sandbox. (2) Analyze and select the most relevant and non-redundant dynamic features for identifying encryptor and lock

SUBMITTER: Herrera-Silva JA 

PROVIDER: S-EPMC9920393 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature