An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices
This addresses cybersecurity threats for IoT device users, but it is incremental as it combines existing techniques like federated learning and neural networks.
The paper tackles the problem of detecting intrusions in IoT devices by proposing a host-based intrusion detection system that uses federated learning and multi-layer perceptron neural networks, achieving high accuracy and enhanced data privacy protection.
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.