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Jul, 2021
适应性入侵检测系统的分割联邦学习
Segmented Federated Learning for Adaptive Intrusion Detection System
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Geet Shingi, Harsh Saglani, Preeti Jain
TL;DR
本文提出了一种基于“分段-联邦学习”(Segmented-FL)的网络入侵检测系统(NIDS),该方法采用定期的本地模型评估和加权聚合本地模型参数的方法来显著提高性能,该方案对于需要协同处理多个不同网络环境数据、并保护个人数据隐私的组织具有重要参考意义。
Abstract
cyberattacks
are a major issues and it causes organizations great financial, and reputation harm. However, due to various factors, the current network intrusion detection systems (NIDS) seem to be insufficent. Predominant NIDS identifies
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