A Multimodal Network Security Framework for Healthcare Based on Deep Learning.

Area of Science:

• Computer Science
• Network Security
• Machine Learning

Background:

• Network security is crucial due to daily life reliance on networks.
• Network flow classification is fundamental for security, monitoring, and Quality of Service (QoS).
• Existing methods rely on manual feature engineering, which is time-consuming and prone to overfitting.

Purpose of the Study:

• To propose a multimodal automatic analysis framework for network flow classification.
• To overcome limitations of manual feature extraction and single-model approaches.
• To improve the efficiency and accuracy of network flow classification.

Main Methods:

• Developed a deep learning-based multimodal framework for automatic feature extraction.
• Utilized both spatial and sequential features for comprehensive analysis.
• Investigated two framework types: pretraining and joint-training.

Main Results:

• The proposed framework automatically extracts spatial and sequential features, suitable for large-flow data.
• Experimental results demonstrate superior performance in accuracy and stability compared to previous methods.
• The framework enhances the efficiency of network flow classification.

Conclusions:

• The multimodal deep learning framework offers an effective solution for automatic network flow classification.
• This approach addresses the challenges of manual feature engineering and single-model limitations.
• The study highlights the potential for improved network security maintenance and monitoring.