Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services
This addresses security issues for network services relying on remote ML models, but appears incremental as it applies an existing autoencoder method to this specific domain.
The paper tackles the problem of protecting machine learning models used for network traffic classification from attacks that affect classification results, proposing a solution based on an autoencoder.
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