A Contrastive Federated Semi-Supervised Learning Intrusion Detection Framework for Internet of Robotic Things

TL;DR

This paper introduces CFedSSL-NID, a federated semi-supervised learning framework that enhances intrusion detection in IoRT environments by leveraging contrastive learning and data augmentation, all while preserving data privacy.

Contribution

It presents a novel federated semi-supervised intrusion detection method for IoRT that effectively uses unlabeled data and privacy-preserving techniques, outperforming existing approaches.

Findings

Outperforms existing federated semi-supervised methods on benchmark datasets.

Requires lower computational resources compared to previous models.

Enhances robustness and accuracy in IoRT intrusion detection.

Abstract

In intelligent industry, autonomous driving and other environments, the Internet of Things (IoT) highly integrated with robotic to form the Internet of Robotic Things (IoRT). However, network intrusion to IoRT can lead to data leakage, service interruption in IoRT and even physical damage by controlling robots or vehicles. This paper proposes a Contrastive Federated Semi-Supervised Learning Network Intrusion Detection framework (CFedSSL-NID) for IoRT intrusion detection and defense, to address the practical scenario of IoRT where robots don't possess labeled data locally and the requirement for data privacy preserving. CFedSSL-NID integrates randomly weak and strong augmentation, latent contrastive learning, and EMA update to integrate supervised signals, thereby enhancing performance and robustness on robots' local unlabeled data. Extensive experiments demonstrate that CFedSSL-NID outperforms existing federated semi-supervised and fully supervised methods on benchmark dataset and has lower resource requirements.