Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges

TL;DR

This paper reviews the use of federated learning for IoT intrusion detection, evaluates its effectiveness with different data distributions, and discusses challenges and future research directions.

Contribution

It provides an empirical evaluation of federated learning-based intrusion detection systems for IoT, considering various data partitioning strategies and aggregation functions.

Findings

Federated learning can detect IoT attacks with varying data distributions.

Different aggregation functions impact detection performance.

The study highlights key challenges for real-world deployment.

Abstract

The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks through an effective and efficient detection process. In the context of the Internet of Things (IoT), most ML-enabled IDS approaches use centralized approaches where IoT devices share their data with data centers for further analysis. To mitigate privacy concerns associated with centralized approaches, in recent years the use of Federated Learning (FL) has attracted a significant interest in different sectors, including healthcare and transport systems. However, the development of FL-enabled IDS for IoT is in its infancy, and still requires research efforts from various areas, in order to identify the main challenges for the deployment in real-world scenarios. In this direction, our work evaluates a FL-enabled IDS approach based on a multiclass classifier considering different data distributions for the detection of different attacks in an IoT scenario. In particular, we use three different settings that are obtained by partitioning the recent ToN\_IoT dataset according to IoT devices' IP address and types of attack. Furthermore, we evaluate the impact of different aggregation functions according to such setting by using the recent IBMFL framework as FL implementation. Additionally, we identify a set of challenges and future directions based on the existing literature and the analysis of our evaluation results.