Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services
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Abstract (arXiv)
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
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 13 references that are in the map. First posted on arXiv 1 May 2025.
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3 of 13 references.
References · 3
- 2024Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Datacited by 11
- 2021Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Casecited by 5
- 2021Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encodercited by 4
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