构建可迁移的联邦入侵检测系统，提升未知攻击识别能力。
Developing a Transferable Federated Network Intrusion Detection System
- 用两阶段预处理与块级智能聚合增强模型迁移性
- 在多个数据集上实现高迁移性能与本地检测率
- 支持不同骨干网络，适合实际分布式安全场景
入侵检测系统（IDS）是网络设备的重要组成部分。本文提出一种基于深度学习的分布式部署入侵检测系统，旨在通过已知攻击知识提升对未知攻击的防御能力。为此，我们设计算法以最大化迁移关系数量，提出一种卷积神经网络（CNN）模型，配合两种优化策略：两阶段数据预处理和块级智能聚合（BBSA）算法。所提系统在保持优异本地检测率的同时，显著提升迁移性能。实验表明该方法具备良好的泛化能力，可在不同数据集间实现迁移，且适用于多种网络架构。代码开源地址：https://github.com/ghosh64/tabfidsv2。
原文摘要 · Abstract (English)
Intrusion Detection Systems (IDS) are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our aim is to better equip deep learning models against unknown attacks using knowledge from known attacks. To this end, we develop algorithms to maximize the number of transferability relationships. We propose a Convolutional Neural Network (CNN) model, along with two algorithms that maximize the number of relationships observed. One is a two step data pre-processing stage, and the other is a Block-Based Smart Aggregation (BBSA) algorithm. The proposed system succeeds in achieving superior transferability performance while maintaining impressive local detection rates. We also show that our method is generalizable, exhibiting transferability potential across datasets and even with different backbones. The code for this work can be found at https://github.com/ghosh64/tabfidsv2.
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。