FLADEN: Federated Learning for Anomaly DEtection in IoT Networks

Abstract

Sensitive applications are strict in terms of data privacy. In this context, intrusion detection systems cannot access the data and analyze it to discover attacks signatures. As a result, it is necessary to analyze data locally without disclosing it to a third party. Machine learning models can achieve this task. This paper proposes a machine-learning framework for intrusion detection on IoT networks. The proposed framework enables participating entities to analyze their data more efficiently and privately. A new real-world dataset is generated using online threat intelligence sources. FLADEN updates the federated learning library to optimize processing time with an accuracy of 99.85%. The proposed framework was applied to machine learning models and shows a precision of 99. 89%, an F1 score of 99. 93%, and a recall of 99.91%. This work presents implications for those researchers who may focus on large-scale anomaly detection with privacy preservation in IoT networks.