用自编码器防御网络服务中的机器学习模型攻击

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

• 基于自编码器构建防护适配器，检测异常流量输入
• 在真实网络数据集上实现92.3%的攻击检测率
• 适合网络安全团队与模型部署工程师使用

随着越来越多任务在远程服务器上执行，识别与分类网络流量成为减轻服务器负载的关键。目前已有多种流量分类方法，本文聚焦于利用机器学习模型解决该问题。然而，这些模型易受攻击，影响网络流量分类结果的准确性。为此，本文提出一种基于自编码器的防护方案，通过重构误差检测恶意输入，在真实网络数据集上实现了92.3%的攻击检测率，有效提升了模型在复杂网络环境中的鲁棒性。

原文摘要 · Abstract (English)

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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