Recent advances in Industry 4.0 technologies drive robotic objects' decentralisation and autonomous intelligence, raising emerging space security concerns, specifically invasion detection. Existing physical detection methods, such as vision-based and radar-based techniques, are ineffective in detecting small-scale objects moving at low speeds. Therefore, it is worth investigating and leveraging the power of artificial intelligence to discover invasion patterns through space data analytics. Additionally, fuzzy modelling is needed for invasion detection to enhance the capability of handling data uncertainty and adaptability to evolving invasion patterns. This study proposes a Blockchain-Enabled Federated Fuzzy Invasion Detection System (BFFIDS) to address these challenges and establish real-time invasion detection capabilities for edge devices in the low earth orbit. The entire model training process is performed over the blockchain and horizontal federated learning scheme, securely reaching consensus in model updates. The system's effectiveness is examined through case analyses on a publicly available dataset. The results indicate that the proposed system can effectively maintain the desired invasion detection performance, with an average Area Under Curve (AUC) value of 0.99 across experimental runs. Utilising the blockchain-based federated learning process, the total size of transmitted data is reduced by 89.5 %, supporting the development of lightweight invasion detection applications. A closed-loop mechanism for continuously updating the space invasion detection model is established to achieve high space security.

中文翻译：

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一种基于区块链的水平联邦学习系统，用于维护空间安全的模糊入侵检测


工业 4.0 技术的最新进展推动了机器人对象的去中心化和自主智能，引发了新出现的太空安全问题，特别是入侵检测。现有的物理检测方法（例如基于视觉和基于雷达的技术）无法有效检测低速移动的小型物体。因此，值得研究和利用人工智能的力量，通过空间数据分析来发现入侵模式。此外，入侵检测需要模糊建模，以增强处理数据不确定性的能力和对不断发展的入侵模式的适应性。本研究提出了一种基于区块链的联邦模糊入侵检测系统 （BFFIDS） 来应对这些挑战，并为近地轨道上的边缘设备建立实时入侵检测能力。整个模型训练过程在区块链和横向联邦学习方案上进行，在模型更新中安全地达成共识。该系统的有效性是通过对公开可用的数据集进行案例分析来检查的。结果表明，所提出的系统可以有效地保持所需的侵袭检测性能，在实验运行中的平均曲线下面积 （AUC） 值为 0.99。利用基于区块链的联邦学习过程，传输数据的总大小减少了 89.5%，支持轻量级入侵检测应用程序的开发。建立空间入侵检测模型不断更新的闭环机制，实现空间高安全性。