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Enhancing Ransomware Detection Using Deep Learning Models
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AI
DOI：10.1007/978-981-96-8892-0_34.png)
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Abstract
En 中文
Ransomware continues to be a critical cybersecurity challenge, causing substantial financial losses and compromising sensitive data across various sectors. Traditional detection methods often fail to address the sophistication and evolving nature of ransomware attacks. This research delves into the application of advanced deep learning algorithms, including Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Region-Based Convolutional Neural Networks (R-CNN), to enhance ransomware detection capabilities. By analyzing network traffic data and system logs, the study demonstrates the unique strengths of these models. RNNs effectively capture sequential and temporal dependencies in data, CNNs excel at extracting spatial patterns, and R-CNNs synergistically combine these capabilities for comprehensive analysis. Comparative evaluations reveal that deep learning models, particularly R-CNNs, not only achieve higher detection accuracy, but also exhibit superior adaptability and efficiency compared to traditional approaches. These findings underscore the transformative potential of deep learning in building robust, adaptive defenses against the ever-evolving landscape of ransomware threats, providing a promising direction for future cybersecurity solutions.
Keywords:
Ransomware Detection
Deep Learning
RNN
CNN
R-CNN
Malware Detection
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