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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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基于人工智能的异常检测技术在加密的流量:一个系统的文献审查.

Il Hwan Ji1, Ju Hyeon Lee1, Min Ji Kang2

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网络攻击越来越多地针对加密通信. 本综述系统地检查了人工智能 (AI) 技术,用于检测运输层安全 (TLS) 加密流量的异常,这是传统方法的挑战.

关键词:
检测异常检测异常检测网络安全 网络安全通过加密的流量来实现.

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科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 在未加密环境中网络攻击的增加需要安全的通信道,如运输层安全 (TLS).
  • 攻击者利用受保护的道,使传统的基于深度包检查 (DPI) 的异常检测对加密流量无效.
  • 人工智能 (AI) 和统计流量分析为检测加密通信中的威胁提供了有希望的替代方案.

研究的目的:

  • 系统地审查现有的基于人工智能 (AI) 的异常检测技术,专门为加密网络流量设计.
  • 识别和分析当前研究中使用的方法,数据集,特征工程和算法.
  • 提供对人工智能驱动的加密交通异常检测最先进技术的全面概述.

主要方法:

  • 根据预先定义的研究问题和资格标准进行了系统的文献审查.
  • 选并评估收集的研究文章的质量,选择了30项相关研究.
  • 根据数据集特征,特征提取/选择,预处理步骤,AI算法和性能指标分析选定的研究.

主要成果:

  • 识别了各种各样的AI技术,用于在加密流量中检测异常.
  • 观察到一些人工智能技术是从未加密的流量分析中进行的调整,而另一些则是对加密环境的新开发.
  • 证实了基于AI的方法的可行性和多样性,用于在受保护的通信道中辨别恶意活动.

结论:

  • 基于人工智能的异常检测是保护加密网络流量的可行和不断发展的领域.
  • 需要进一步的研究来探索针对加密数据复杂性的独特AI技术.
  • 这些发现为旨在加强加密通信环境中的网络安全的研究人员和从业人员提供了宝贵的资源.