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Therapeutic Drug Monitoring: Overview and Classification01:16

Therapeutic Drug Monitoring: Overview and Classification

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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...
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Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

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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.
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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...
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Updated: May 3, 2026

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MTL-DoHTA: Multi-Task Learning-Based DNS over HTTPS Traffic Analysis for Enhanced Network Security.

Woong Kyo Jung1, Byung Il Kwak1

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

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

This study introduces MTL-DoHTA, a novel framework for analyzing encrypted DNS over HTTPS (DoH) traffic. It accurately detects malicious activity and identifies DNS tunneling tools, enhancing network security.

Keywords:
DNS covert channelDNS over HTTPSdeep learningmulti-task learning

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Area of Science:

  • Cybersecurity
  • Network Security
  • Machine Learning

Background:

  • DNS over HTTPS (DoH) enhances privacy by encrypting DNS queries.
  • Encrypted DoH traffic poses challenges for detecting malicious activities like DNS tunneling.
  • Existing methods struggle to effectively analyze and classify DoH traffic for security purposes.

Purpose of the Study:

  • To propose MTL-DoHTA, a multi-task learning framework for analyzing DoH traffic.
  • To classify DoH vs. non-DoH traffic, benign vs. malicious DoH traffic, and identify specific DNS tunneling tools.
  • To enhance the security of sensor-based network systems against sophisticated threats.

Main Methods:

  • Utilized statistical features from network traffic.
  • Employed a 2D-Convolutional Neural Network (2D-CNN) architecture.
  • Incorporated GradNorm and attention mechanisms for enhanced performance.
  • Applied downsampling techniques to handle class imbalance and mitigate overfitting.

Main Results:

  • Achieved a macro-averaging F1-score of 0.9905 on the CIRA-CIC-DoHBrw-2020 dataset.
  • Demonstrated effective handling of class imbalance and overfitting.
  • Successfully classified DoH vs. non-DoH traffic, benign vs. malicious DoH traffic, and identified DNS tunneling tools.

Conclusions:

  • MTL-DoHTA provides a reliable method for monitoring and securing sensor networks.
  • The framework effectively detects sophisticated threats within encrypted DoH traffic.
  • MTL-DoHTA shows potential for enhancing multi-tasking capabilities in resource-constrained environments.