A Clustering Strategy for Enhanced FL-Based Intrusion Detection in IoT Networks

Abstract

The Internet of Things (IoT) is growing rapidly and so the need of ensuring protection against cybersecurity attacks to IoT devices. In this scenario, Intrusion Detection Systems (IDSs) play a crucial role and data-driven IDSs based on machine learning (ML) have recently attracted more and more interest by the research community. While conventional ML-based IDSs are based on a centralized architecture where IoT devices share their data with a central server for model training, we propose a novel approach that is based on federated learning (FL). However, conventional FL is ine ective in the considered scenario, due to the high statistical heterogeneity of data collected by IoT devices. To overcome this limitation, we propose a three-tier FL-based architecture where IoT devices are clustered together based on their statistical properties. Clustering decisions are taken by means of a novel entropy-based strategy, which helps improve model training performance. We tested our solution on the CIC-ToN-IoT dataset: our clustering strategy increases intrusion detection performance with respect to a conventional FL approach up to +17% in terms of F1-score, along with a significant reduction of the number of training rounds.

Enhancing Security in Satellite-Based IoT-HPC Cloud Architectures: A Hybrid AI based Security Framework Approach

“…These challenges poses significant hurdles regarding processing power and attack detection. Moreover, IoT devices may be situated in areas with limited connectivity, making it impractical to transmit large volumes of data to a central server for analysis [6] [7] . We present Embedded Hybrid Intrusion Detection EHID, a novel solution designed to tackle the challenges associated with intrusion detection in IoT devices as mentioned in [7].…”

“…Moreover, IoT devices may be situated in areas with limited connectivity, making it impractical to transmit large volumes of data to a central server for analysis [6] [7] . We present Embedded Hybrid Intrusion Detection EHID, a novel solution designed to tackle the challenges associated with intrusion detection in IoT devices as mentioned in [7]. EHID addresses the limitations of limited resources and connectivity, offering efficient and effective intrusion detection capabilities.…”