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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

875
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
875

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Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes
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A user DNS fingerprint dataset.

Josef Zápotocký1, Jan Fiala2, Jan Fesl1

  • 1Department of Computer Systems, Faculty of Information Technology, Czech Technical University in Prague, Czech Republic.

Data in Brief
|April 22, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new dataset for identifying network users via DNS fingerprints, enabling user tracking without IP addresses. This resource aids machine learning model development for enhanced network user identification.

Keywords:
DNSFingerprintIdentificationMachine learningUser

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Area of Science:

  • Computer Science
  • Network Security
  • Data Science

Background:

  • User identification in networks often relies on IP addresses, which can be dynamic or masked.
  • Existing methods for user tracking may require additional network location information.
  • Deep analysis of user behavior necessitates robust identification techniques beyond IP-based methods.

Purpose of the Study:

  • To introduce a novel dataset for user identification based solely on DNS fingerprints.
  • To facilitate the creation of machine learning models for user identification without IP addresses.
  • To provide a reference dataset for developing and validating DNS fingerprinting techniques.

Main Methods:

  • Collected a large dataset from real metropolitan ISP network traffic.
  • Compiled 2.3 billion DNS queries from 6.2 million unique domain names over three months.
  • Developed a dataset with 1137 classification attributes, including content categories accessed by users.

Main Results:

  • Created a comprehensive dataset enabling user identification through DNS fingerprints.
  • The dataset details user activity, including daily and 24-hour statistics.
  • Unique classification of user activity based on accessed content categories is included.

Conclusions:

  • The new dataset supports the development of machine learning models for user identification independent of IP addresses.
  • It serves as a valuable resource for creating and referencing user-specific DNS fingerprints.
  • Enables in-depth examination of monitored network user behavior through DNS analysis.