Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services

Denis Parfenov, Anton Parfenov
5/1/2025
cs.CRcs.LG

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Abstract

Due to the increasing number of tasks that are solved on remote servers, identifying and classifying traffic is an important task to reduce the load on the server. There are various methods for classifying traffic. This paper discusses machine learning models for solving this problem. However, such ML models are also subject to attacks that affect the classification result of network traffic. To protect models, we proposed a solution based on an autoencoder

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Cite this paper

@article{parfenov2025development,
  title  = {Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services},
  author = {Denis Parfenov and Anton Parfenov},
  year   = {2025},
  eprint = {2505.01460},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2505.01460v1}
}

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