An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices

TL;DR

This paper proposes a federated learning-based host intrusion detection system using neural networks to improve IoT device security and privacy.

Contribution

It introduces a novel federated learning approach combined with neural networks for IoT intrusion detection, addressing privacy concerns.

Findings

High detection accuracy achieved

Enhanced data privacy protection

Effective in identifying cyberattacks on IoT devices

Abstract

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.