Cyber Physical Awareness via Intent-Driven Threat Assessment: Enhanced Space Networks with Intershell Links

2025-08-22

AI Summary

Existing spatial network threat assessment methods model reliability and security in isolation, leading to overfitting on specific criteria and poor adaptability to complex, cross-layer threat scenarios. Method: This paper proposes an intent-driven Cyber-Physical Awareness (CPA) framework—the first to jointly model threat capability and malicious intent—enabling synergistic reliability and security evaluation. We design a multi-task deep learning architecture that simultaneously extracts signal features and jointly predicts reliability metrics and adversarial intent; a tunable-threshold mechanism is introduced to dynamically accommodate heterogeneous security requirements. Results: Experiments in inter-satellite link environments demonstrate that the framework significantly improves threat detection robustness: false positive rate decreases by 23.6% and false negative rate by 18.4% compared to conventional sequential methods, substantially enhancing comprehensive threat perception for emerging inter-layer-coupled space networks.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Multiagent Systems: Adversarial Agents
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

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.

Problem

• Develops intent-driven threat models for space networks
• Proposes holistic cyber-physical awareness framework against overfitting
• Enhances threat detection robustness with multitask learning architecture

Innovation

• Intent-driven threat models with capabilities and intents
• Multitask learning architecture for reliability and intention analysis
• Adaptable threat assessment aligning with varying security requirements