Federated Learning Approach for Distributed Ransomware Analysis

Authors: Aldin Vehabovic, Hadi Zanddizari, Farook Shaikh, Nasir Ghani, Morteza Safaei Pour, Elias Bou-Harb, Jorge Crichigno
Published: 2023-06-25

Source: https://arxiv.org/abs/2306.14090

PDF: https://arxiv.org/pdf/2306.14090

Labels Predicted by AI

連合学習 マルウェア検出手法 マルウェア分類

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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.