低地球轨道卫星系统中网络攻击检测的时间与多模态深度学习
Temporal and Multimodal Deep Learning for Cyberattack Detection in LEO Satellite Systems
- Center for Cybersecurity and AI（网络安全与人工智能中心）
- University of West Florida（西佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用UNSW-IoTSAT数据集，通过子系统融合MLP和分层多模态Transformer等结构化深度学习架构，系统评估LEO卫星网络攻击检测，分层Transformer在抗泄漏协议下达到91.66%准确率。
AI中文摘要：
对低地球轨道（LEO）卫星通信系统的日益依赖，增加了对能够在复杂动态空间环境中检测网络攻击的智能方法的需求。与传统网络入侵检测不同，卫星系统在射频（RF）链路、星载硬件和轨道运行中产生异构信息。然而，许多现有方法要么依赖地面入侵检测数据集，要么独立评估单个观测值，限制了其捕获LEO卫星特定时间攻击行为的能力。在本工作中，我们利用近期引入的卫星专用UNSW-IoTSAT数据集，对基于深度学习的网络攻击检测进行了系统性研究。我们研究了保留硬件、轨道和RF信息的结构化学习架构，包括子系统融合多层感知机（Subsystem-Fusion MLP）和分层多模态Transformer，后者同时对跨子系统交互和时间演化进行建模。我们进一步评估了抗泄漏的行级和时间设置，以及跨卫星泛化能力，以表征模型架构和评估协议如何影响卫星网络攻击检测。实验结果表明了结构化多模态建模和严格评估的价值，其中分层Transformer在抗泄漏评估协议下达到了高达91.66%的准确率和85.63%的宏F1分数。
英文摘要：
The growing reliance on Low-Earth Orbit (LEO) satellite communication systems has increased the need for intelligent methods capable of detecting cyberattacks across complex and dynamic space environments. Unlike conventional network intrusion detection, satellite systems generate heterogeneous information across radio-frequency (RF) links, onboard hardware, and orbital operations. However, many existing approaches either rely on terrestrial intrusion datasets or evaluate individual observations independently, limiting their ability to capture temporal attack behavior specific to LEO satellites. In this work, we conduct a systematic study of deep-learning-based cyberattack detection using the recently introduced satellite-specific UNSW-IoTSAT dataset. We investigate structured learning architectures that preserve hardware, orbital, and RF information, including a Subsystem-Fusion MLP and a hierarchical multimodal Transformer that models both cross-subsystem interactions and temporal evolution. We further evaluate leakage-resistant row-level and temporal settings, along with cross-satellite generalization, to characterize how model architecture and evaluation protocol influence satellite cyberattack detection. Experimental results demonstrate the value of structured multimodal modeling and rigorous evaluation, with the hierarchical Transformer achieving up to 91.66% accuracy and 85.63% macro F1 under the leakage-resistant evaluation protocol.
