The current amount of IoT devices and their limitations has come to serve as a motivation for malicious entities to take advantage of such devices and use them for their own gain. To protect against cyberattacks in IoT devices, Machine Learning techniques can be applied to Intrusion Detection Systems. Moreover, privacy related issues associated with centralized approaches can be mitigated through Federated Learning. This work proposes a Host-based Intrusion Detection Systems that leverages Federated Learning and Multi-Layer Perceptron neural networks to detected cyberattacks on IoT devices with high accuracy and enhancing data privacy protection.

翻译：当前物联网设备的数量及其局限性已成为恶意实体利用此类设备谋取私利的诱因。针对物联网设备的网络攻击，可将机器学习技术应用于入侵检测系统。此外，通过联邦学习可缓解集中式方法带来的隐私问题。本研究提出一种基于主机的入侵检测系统，利用联邦学习与多层感知器神经网络，以高准确率检测物联网设备上的网络攻击，并增强数据隐私保护。