基于深度学习的医疗保健多式联网安全框架
Qiang Qiang Chen1, Jian Ping Li1, Amin Ul Haq1
1School of Computer Science and Engineering, University of Electronic Science and Technology China, Chengdu 611731, China.
Computational intelligence and neuroscience
|September 16, 2024
概括
本研究介绍了一个深度学习框架,用于使用空间和顺序特征自动分类网络流. 与手动方法相比,它提高了效率和准确性,改善了网络安全分析.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 由于日常生活依赖网络,网络安全至关重要.
- 网络流量分类对于安全,监控和服务质量 (QoS) 来说至关重要.
- 现有的方法依赖于手动特征工程,这耗时且容易过度装配.
研究的目的:
- 为网络流量分类提出一个多式联网自动分析框架.
- 克服手动特征提取和单一模型方法的局限性.
- 提高网络流量分类的效率和准确性.
主要方法:
- 开发了一个基于深度学习的多式联络框架,用于自动提取特征.
- 利用空间和顺序特征进行全面分析.
- 研究了两种框架类型:预培训和联合培训.
主要成果:
- 拟议的框架自动提取空间和顺序特征,适合大流数据.
- 实验结果表明,与以前的方法相比,在准确性和稳定性方面表现优越.
- 该框架提高了网络流量分类的效率.
结论:
- 多式联网深度学习框架为自动网络流量分类提供了有效的解决方案.
- 这种方法解决了手动特征工程和单一模型限制的挑战.
- 该研究强调了改善网络安全维护和监控的潜力.
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