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Cyberattacks Defense in Digital Music Streaming Platforms by Mobile Distributed Machine Learning.

Guoxu Fan1

  • 1Pingdingshan University, Conservatory of Music, Pingdingshan, Henan 467000, China.

Computational Intelligence and Neuroscience
|May 16, 2022
PubMed
Summary

This study introduces a mobile distributed machine learning model to defend digital music platforms against cyberattacks. This approach enhances security by leveraging mobile devices for complex data processing, improving defense capabilities.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Digital music platforms face increasing cyber threats due to their popularity.
  • Traditional machine learning models struggle with large datasets and complex computations on single machines.
  • Mobile devices offer a potential solution for distributed processing.

Purpose of the Study:

  • To propose an intelligent cyberattack defense model for digital music streaming platforms.
  • To leverage mobile distributed machine learning (MDML) to alleviate server workload.
  • To enhance the security and stability of digital music platforms against cyber threats.

Main Methods:

  • Development of a mobile distributed machine learning (MDML) system.
  • Implementation of a distributed logit polynomial function model.

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  • Application of distributed binary regression for accounting units.

    • Main Results:

    • The proposed MDML model effectively handles large datasets and complex computations.
    • The distributed logit polynomial function model demonstrates high stability in noisy environments.
    • Reduced server workload and enhanced cyberattack defense capabilities were observed.

    Conclusions:

    • Mobile distributed machine learning offers a viable solution for securing digital music platforms.
    • The proposed model provides a stable and efficient method for cyberattack defense.
    • This approach enhances the overall security posture of the digital music industry.