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In recent years, ransomware attacks have grown dramatically. New variants continually emerging make tracking and mitigating these threats increasingly difficult using traditional detection methods. As the landscape of ransomware evolves, there is a growing need for more advanced detection techniques. Neural networks have gained popularity as a method to enhance detection accuracy, by leveraging low-level hardware information such as hardware events as features for identifying ransomware attacks. In this paper, we investigated several state-of-the-art supervised learning models, including XGBoost, LightGBM, MLP, and CNN, which are specifically designed to handle time series data or image-based data for ransomware detection. We compared their detection accuracy, computational efficiency, and resource requirements for classification. Our findings indicate that particularly LightGBM, offer a strong balance of high detection accuracy, fast processing speed, and low memory usage, making them highly effective for ransomware detection tasks.
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Federated Learning Approach for Distributed Ransomware Analysis
Ransomware is a form of malware that uses encryption methods to prevent legitimate users from accessing their data files. To date, many ransomware families have been released, causing immense damage and financial losses for private users, corporations, and governments. As a result, researchers have proposed a range of ransomware detection schemes using various machine learning (ML) methods to analyze binary files and action sequences. However as this threat continues to proliferate, it is becoming increasingly difficult to collect and analyze massive amounts of ransomware executables and trace data at a common site (due to data privacy and scalability concerns). Hence this paper presents a novel distributed ransomware analysis (DRA) solution for detection and attribution using the decentralized federated learning (FL) framework. Detailed performance evaluation is then conducted for the case of static analysis with rapid/lightweight feature extraction using an up-to-date ransomware repository. Overall results confirm the effectiveness the FL-based solution.
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- Award ID(s):
- 2219773
- PAR ID:
- 10542143
- Publisher / Repository:
- Springer, Cham
- Date Published:
- ISSN:
- 1611-3349
- ISBN:
- 978-3-031-41181-6
- Page Range / eLocation ID:
- 621-641
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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