Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning
This addresses data privacy and security challenges in IoV systems, but it is incremental as it applies existing techniques to a specific domain.
The paper tackles intrusion detection in Internet of Vehicles by proposing a federated learning framework that uses SMOTE, outlier detection, and hyperparameter tuning, achieving high detection performance while protecting sensitive data.
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.