This review paper looks at recent research on Federated Learning (FL) for Collaborative Intrusion Detection Systems (CIDS) to establish a taxonomy and survey. The motivation behind this review comes from the difficulty of detecting coordinated cyberattacks in large-scale distributed networks. Collaborative anomalies are one of the network anomalies that need to be detected through robust collaborative learning methods. FL is promising collaborative learning method in recent research. This review aims to offer insights and lesson learn for creating a taxonomy of collaborative anomaly detection in CIDS using FL as a collaborative learning method. Our findings suggest that a taxonomy is required to map the discussion area, including an algorithm for training the learning model, the dataset, global aggregation model, system architecture, security, and privacy. Our results indicate that FL is a promising approach for collaborative anomaly detection in CIDS, and the proposed taxonomy could be useful for future research in this area. Overall, this review contributes to the growing knowledge of FL for CIDS, providing insights and lessons for researchers and practitioners. This research also concludes significant challenges, opportunities, and future directions in CIDS based on collaborative anomaly detection using FL.

中文翻译：

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基于联邦学习的协作入侵检测系统的分类与调查


本综述论文着眼于最近关于协作入侵检测系统 （CIDS） 的联邦学习 （FL） 的研究，以建立分类和调查。本综述背后的动机来自于在大规模分布式网络中检测协调网络攻击的难度。协作异常是需要通过强大的协作学习方法检测的网络异常之一。在最近的研究中，联邦学习是一种很有前途的协作学习方法。本综述旨在为使用 FL 作为协作学习方法在 CIDS 中创建协作异常检测分类法提供见解和经验教训。我们的研究结果表明，需要一个分类法来映射讨论区域，包括用于训练学习模型的算法、数据集、全局聚合模型、系统架构、安全性和隐私。我们的结果表明，FL 是一种很有前途的 CIDS 协作异常检测方法，所提出的分类法可能对该领域的未来研究有用。总体而言，本综述有助于增加对 CIDS 的 FL 知识，为研究人员和从业者提供见解和经验教训。本研究还总结了基于使用 FL 的协作异常检测的 CIDS 的重大挑战、机遇和未来方向。