Data-Centric Machine Learning Approach for Early Ransomware Detection and Attribution
Cryptography and Security
2023-06-27 v1
Abstract
Researchers have proposed a wide range of ransomware detection and analysis schemes. However, most of these efforts have focused on older families targeting Windows 7/8 systems. Hence there is a critical need to develop efficient solutions to tackle the latest threats, many of which may have relatively fewer samples to analyze. This paper presents a machine learning(ML) framework for early ransomware detection and attribution. The solution pursues a data-centric approach which uses a minimalist ransomware dataset and implements static analysis using portable executable(PE) files. Results for several ML classifiers confirm strong performance in terms of accuracy and zero-day threat detection.
Cite
@article{arxiv.2305.13287,
title = {Data-Centric Machine Learning Approach for Early Ransomware Detection and Attribution},
author = {Aldin Vehabovic and Hadi Zanddizari and Nasir Ghani and Farooq Shaikh and Elias Bou-Harb and Morteza Safaei Pour and Jorge Crichigno},
journal= {arXiv preprint arXiv:2305.13287},
year = {2023}
}
Comments
6 pages, 5 figures