Heterogeneous Data-Aware Federated Learning for Intrusion Detection Systems via Meta-Sampling in Artificial Intelligence of Things

Intrusion detection systems (IDSs) integrated with machine learning (ML) techniques have proven to be effective defenses against the increasing cybersecurity attacks in the Artificial Intelligence of Things (AIoT) domain. Privacy concerns have prompted the emergence of federated learning (FL) as a promising solution for AIoT intrusion detection. Despite their potential, FL-based IDSs still face challenges related to class-imbalanced data and Non-Independent and Identically Distributed (non-IID) data among AIoT devices. These challenges hinder FL from learning meaningful features from the data, thus impeding the convergence of the learning process. To tackle these issues, this article proposes a clustering-enabled federated meta-training (CFMT) framework for AIoT intrusion detection. The proposed CFMT framework effectively addresses the negative impact of imbalanced and non-IID data. Specifically, we design a data- and model-agnostic meta-sampler that adaptively balances local data sets, thereby mitigating the data imbalance problem. Additionally, we propose a dynamic clustering algorithm that selectively eliminates the local models affected by the training state bias caused by non-IID data, thereby addressing the non-IID data issue. Extensive case studies on two real-world data sets demonstrate the superior performance of the proposed CFMT framework compared to existing solutions, including federated non-IID algorithms and federated imbalanced learning algorithms, in terms of IDS performance. Our code and data are available at
https://gitee.com/mindspore/models/tree/master/research/cv/HDFL-IDS-Meta
.