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

TL;DR

This paper introduces a comprehensive framework for cyber physical awareness in space networks, utilizing intent-driven threat models and multitask learning to improve threat detection and assessment robustness.

Contribution

It presents a novel holistic framework that combines intent and capability analysis with adaptable threat assessment for space networks with intershell links.

Findings

Outperforms conventional sequential threat assessment methods.

Enhances robustness of threat detection in space networks.

Effectively addresses complex threat scenarios.

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.