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相关概念视频

Therapeutic Drug Monitoring: Overview and Classification01:16

Therapeutic Drug Monitoring: Overview and Classification

642
Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...
642
Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

429
Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
429
Therapeutic Drug Monitoring: Drug Analysis Methods01:26

Therapeutic Drug Monitoring: Drug Analysis Methods

342
Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood or body tissues to tailor drug therapy effectively. This monitoring is critical for managing drugs with narrow therapeutic indices like digoxin and phenytoin, ensuring they are both safe and effective. For instance, monitoring theophylline levels in asthma patients involves precision and sensitivity to adjust doses according to individual responses to therapy, ensuring efficacy and...
342

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MTL-DoHTA:基于多任务学习的DNS在HTTPS上的流量分析,用于增强网络安全.

Woong Kyo Jung1, Byung Il Kwak1

  • 1Division of Software, Hallym University, Chuncheon 24252, Republic of Korea.

Sensors (Basel, Switzerland)
|February 26, 2025
PubMed
概括

本研究介绍了MTL-DoHTA,这是一种用于分析通过HTTPS (DoH) 流量的加密DNS的新框架. 它准确地检测恶意活动,并识别DNS道工具,增强网络安全.

科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 通过加密DNS查询,DNS over HTTPS (DoH) 通过加密DNS查询来提高隐私.
  • 加密的DoH流量对检测恶意活动 (如DNS道) 构成挑战.
  • 现有的方法很难有效地分析和分类DoH流量以达到安全目的.

研究的目的:

  • 提出MTL-DoHTA,一个多任务学习框架来分析DoH流量.
  • 为了分类DoH与非DoH流量,良性与恶意DoH流量,并识别特定的DNS道工具.
  • 提高基于传感器的网络系统对复杂威胁的安全性.

主要方法:

  • 利用来自网络流量的统计特征.
  • 采用了二维卷积神经网络 (2D-CNN) 架构.
  • 整合了GradNorm和注意力机制,以提高表现.
  • 应用下方采样技术来处理类不平衡并减轻过度拟合.

主要成果:

  • 在CIRA-CIC-DoHBrw-2020数据集上获得了0.9905的宏观平均F1得分.
  • 证明有效处理类不平衡和过度装配.
关键词:
在DNS中,隐藏的道是DNS的秘密道.在HTTPS上使用DNS.深度学习是一种深度学习.多任务学习是多任务学习.

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  • 成功分类了DoH与非DoH流量,良性与恶意DoH流量,并确定了DNS道工具.
  • 结论:

    • MTL-DoHTA提供了一种可靠的监控和保护传感器网络的方法.
    • 该框架有效地检测到DoH加密流量中的复杂威胁.
    • 在资源有限的环境中,MTL-DoHTA显示了增强多任务能力的潜力.