FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning

Over the last decade, Internet of Things (IoT) has permeated our daily life with a broad range of applications. However, a lack of adequate security in IoT devices renders IoT systems vulnerable to various network-based cyberattacks, potentially causing severe damage. Recent works have explored using machine learning to build anomaly detection models for defending against such attacks. In this paper, we propose FeCo, a federated-contrastive-learning framework that coordinates in-network IoT devices to jointly learn intrusion detection models. FeCo utilizes federated learning to alleviate users’ privacy concerns as participating devices only submit their model parameters rather than raw local data. Compared to previous works, we develop a novel representation learning method based on contrastive learning that is able to learn a more accurate model for the benign class. FeCo significantly improves the intrusion detection accuracy compared to previous works. In addition, we implement a two-step feature selection scheme to avoid overfitting and reduce computation time. Through extensive experiments on the NSL-KDD dataset and the BaIoT dataset, we demonstrate that FeCo achieves as high as 8% accuracy improvement compared to the state-of-the-art and is robust to non-independent and identically distributed (non-IID) data. Our implementation of FeCo on a Raspberry Pi device further confirms the applicability of FeCo for resource-constrained IoT devices.