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A Contrastive Federated Semi-Supervised Learning Intrusion Detection Framework for Internet of Robotic Things (2505.06636v1)

Published 10 May 2025 in cs.CR

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.

Summary

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

The paper presents a novel intrusion detection framework combining contrastive federated learning and semi-supervised learning approaches, specifically designed for the Internet of Robotic Things (IoRT). This framework, named CFedSSL-NID, addresses cybersecurity threats in IoRT environments where data privacy is crucial, and labeled data is scarce.

Key Contributions and Methodology

The primary contribution of this research is the development of CFedSSL-NID, which integrates federated learning (FL), semi-supervised learning (SSL), and contrastive learning (CL) to enhance intrusion detection capabilities without compromising data privacy. The proposed method leverages FL to allow distributed training across decentralized IoRT devices, ensuring that data remains on local devices to preserve privacy. SSL is applied to utilize unlabeled data available on IoRT devices, which commonly lack labeled intrusion data due to constraints in computational and storage resources.

CFedSSL-NID incorporates randomly weak and strong data augmentation techniques along with dropout to improve the model's generalization and robustness. The framework applies latent contrastive learning on the robot clients to exploit the unlabeled data effectively, creating more comprehensive data representations by contrasting positive sample pairs in a latent space. This allows the model to distinguish between similar and dissimilar samples without requiring labeled data at the client side. The server-side model is updated using an Exponential Moving Average (EMA), integrating supervised signals from the server's labeled dataset. This approach ensures the model captures generalized features from self-supervised learning while aligning them with supervised classification tasks.

Experimental Results

The paper reports significant improvements in accuracy, precision, recall, and F1-score over existing methods, validating the effectiveness of CFedSSL-NID. The framework outperforms other federated semi-supervised and fully supervised learning methods on network intrusion detection tasks using the NSL-KDD dataset. Notably, CFedSSL-NID successfully addresses the challenge of imbalanced data distribution typically observed in intrusion traffic datasets, demonstrating robust performance across both majority and minority classes.

Implications and Future Work

CFedSSL-NID has substantial implications for enhancing cybersecurity in IoRT environments, promoting privacy-preserving and efficient intrusion detection solutions. By utilizing federated learning, CFedSSL-NID allows for scalable and secure deployment in resource-constrained settings, such as industrial automation and autonomous driving, where IoRT plays a critical role.

Future improvements could focus on optimizing the framework's complexity for deployment on devices with limited computational capabilities and exploring encryption techniques to further enhance data privacy. Real-world testing and deployments in actual robotic networks will be essential for evaluating CFedSSL-NID's practical efficacy and its impact on IoRT cybersecurity.

Overall, this research contributes a sophisticated detection framework that integrates modern machine learning paradigms, offering a solution to pressing security challenges in the expanding field of IoRT.