Federated learning is a technique of decentralized machine learning. that allows multiple parties to collaborate and learn a shared model without sharing their raw data. Our paper proposes a federated learning framework for intrusion detection in Internet of Vehicles (IOVs) using the CIC-IDS 2017 dataset. The proposed framework employs SMOTE for handling class imbalance, outlier detection for identifying and removing abnormal observations, and hyperparameter tuning to optimize the model's performance. The authors evaluated the proposed framework using various performance metrics and demonstrated its effectiveness in detecting intrusions with other datasets (KDD-Cup 99 and UNSW- NB-15) and conventional classifiers. Furthermore, the proposed framework can protect sensitive data while achieving high intrusion detection performance.

翻译：联邦学习是一种去中心化机器学习技术，允许多个参与方在不共享原始数据的情况下协作学习共享模型。本文提出了一种基于联邦学习的车联网（IOVs）入侵检测框架，采用CIC-IDS 2017数据集进行验证。该框架通过SMOTE方法处理类别不平衡问题，利用异常检测技术识别并剔除异常观测值，并运用超参数调优以优化模型性能。作者采用多种性能指标对框架进行评估，并验证了其在KDD-Cup 99和UNSW-NB-15等其他数据集上，相较于传统分类器表现出显著的有效性。此外，该框架能够在保护敏感数据的同时实现高入侵检测性能。