A Privacy-Preserving Federated Learning for IoT Intrusion Detection System

Abstract

The Internet of Things (IoT) is an impending area with applications in numerous fields. The number of IoT devices has seen exponential growth, increasing apprehensions around security. Cyberattacks are of rising concern because of the expanded attack surface of threats that have plagued networks. Adding to that are insecure practices among users who may not know to protect their IoT devices. Therefore, IoT security has become fundamental, especially as IoT devices carry sensitive data. This paper provides a proof of concept of an Intelligent Intrusion Detection System for IoT. We centered our work on a privacy-preserving approach offering a Federated Learning (FL) based solution for intrusions recognition combining network and energy data. Our model has achieved high accuracy while preserving a short running time in multiple FL rounds.