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

-- Due to the increasing number of tasks that are solved on remote servers, identifying and classifyi ng traffic is an important task to reduce the load on the server. There are various methods for classifying traffic. 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. The threat landscape for industrial systems is rapidly evolving, with attack s becoming targeted and motivated.