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

TL;DR

This paper presents an autoencoder-based adapter to analyze and protect machine learning models used for network traffic classification against attacks, addressing security concerns in remote server tasks.

Contribution

It introduces a novel autoencoder-based adapter to enhance the security of ML models in network traffic analysis, a new approach in this context.

Findings

The adapter improves model robustness against attacks.

The autoencoder effectively detects malicious traffic.

Enhanced security for remote server ML applications.

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