用特征嵌入替代位置编码，提升联邦学习下的网络入侵检测效果
FetFIDS: A Feature Embedding Attention based Federated Network Intrusion Detection Algorithm
- 用特征嵌入替代传统位置编码，增强模型对网络流量特征的捕捉能力
- 在联邦学习环境下，检测准确率优于多个主流入侵检测系统
- 适合边缘计算场景，兼顾隐私保护与本地性能优化
入侵检测系统（IDS）在防止恶意攻击者利用网络漏洞方面发挥着日益重要的作用。近年来，基于深度学习的方法显著提升了IDS的性能。本文提出FetFIDS，探索使用特征嵌入替代位置嵌入，以提升基于Transformer的深度学习系统在入侵检测中的表现。该模型专为边缘学习场景设计，通过多轮联邦学习实现隐私保护与本地性能提升。FetFIDS在联邦环境中优于多个先进入侵检测系统，展现出高度适配联邦学习的特点。代码已开源：https://github.com/ghosh64/fetfids。
原文摘要 · Abstract (English)
Intrusion Detection Systems (IDS) have an increasingly important role in preventing exploitation of network vulnerabilities by malicious actors. Recent deep learning based developments have resulted in significant improvements in the performance of IDS systems. In this paper, we present FetFIDS, where we explore the employment of feature embedding instead of positional embedding to improve intrusion detection performance of a transformer based deep learning system. Our model is developed with the aim of deployments in edge learning scenarios, where federated learning over multiple communication rounds can ensure both privacy and localized performance improvements. FetFIDS outperforms multiple state-of-the-art intrusion detection systems in a federated environment and demonstrates a high degree of suitability to federated learning. The code for this work can be found at https://github.com/ghosh64/fetfids.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。