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arXiv · 2306.14090

Federated Learning Approach for Distributed Ransomware Analysis

Aldin Vehabovic·Hadi Zanddizari·Farook Shaikh·Nasir Ghani·Morteza Safaei Pour·Elias Bou-Harb·Jorge Crichigno
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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.

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Aldin Vehabovic, Hadi Zanddizari, Farook Shaikh, Nasir Ghani, Morteza Safaei Pour, Elias Bou-Harb, Jorge Crichigno. 2023-06-25. Federated Learning Approach for Distributed Ransomware Analysis. https://arxiv.org/abs/2306.14090

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