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- Title
Ransomware Detection Model Based on Adaptive Graph Neural Network Learning †.
- Authors
Li, Jun; Yang, Gengyu; Shao, Yanhua
- Abstract
Ransomware is a type of malicious software that encrypts or locks user files and demands a high ransom. It has become a major threat to cyberspace security, especially as it continues to be developed and updated at exponential rates. Ransomware detection technology has become a focus of research on information security risk detection methods. However, current ransomware detection techniques have high false positive and false negative rates, and traditional methods ignore global word co-occurrence and correlation information between key node steps in the entire process. This poses a significant challenge for accurately identifying and detecting ransomware. We propose a ransomware detection model based on co-occurrence information adaptive diffusion learning using a Text Graph Convolutional Network (ADC-TextGCN). Specifically, ADC-TextGCN first assign self-weights to word nodes based on sensitive API call functions and preserve co-occurrence information using Point Mutual Information Theory (COIR-PMI); then our model automatically learn the optimal neighborhood through an Adaptive Diffusion Convolution (ADC) strategy, thereby improving the ability to aggregate long-distance node information across layers and enhancing the network's ability to represent ransomware behavior. Experimental results show that our method achieves an accuracy of over 96.6% in ransomware detection, proving its effectiveness and superiority compared to traditional methods based on CNN and RNN in ransomware detection.
- Subjects
RANSOMWARE; GRAPH neural networks; MALWARE; INFORMATION technology security; INFORMATION dissemination
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 11, p4579
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14114579