通过意图驱动模型提升太空网络威胁感知能力
Cyber Physical Awareness via Intent-Driven Threat Assessment: Enhanced Space Networks with Intershell Links
- 提出三步框架：信号特征提取、多任务学习判别可靠性与意图
- 多任务架构使威胁检测准确率显著优于传统串行方法
- 适用于具备跨轨道链路的复杂太空网络，兼顾安全与可靠性
本文针对太空网络中的威胁评估问题，提出一种基于意图驱动的威胁建模框架。传统将可靠性和安全性分开分析的做法可能导致系统特定条件下的过拟合。所提框架分为三步：首先，设计算法提取接收信号的特征属性，以直观理解潜在威胁；其次，构建多任务学习架构，一个任务评估可靠性相关能力，另一个任务解析信号背后的意图；最后，提出可适配不同安全与可靠性需求的动态威胁评估机制。该框架增强了威胁检测与评估的鲁棒性，在具有新兴跨壳层链路的太空网络中，有效应对复杂威胁场景，性能优于传统串行方法。
原文摘要 · Abstract (English)
This letter addresses essential aspects of threat assessment by proposing intent-driven threat models that incorporate both capabilities and intents. We propose a holistic framework for cyber physical awareness (CPA) in space networks, pointing out that analyzing reliability and security separately can lead to overfitting on system-specific criteria. We structure our proposed framework in three main steps. First, we suggest an algorithm that extracts characteristic properties of the received signal to facilitate an intuitive understanding of potential threats. Second, we develop a multitask learning architecture where one task evaluates reliability-related capabilities while the other deciphers the underlying intentions of the signal. Finally, we propose an adaptable threat assessment that aligns with varying security and reliability requirements. The proposed framework enhances the robustness of threat detection and assessment, outperforming conventional sequential methods, and enables space networks with emerging intershell links to effectively address complex threat scenarios.
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