Cyber Physical Awareness via Intent-Driven Threat Assessment: Enhanced Space Networks with Intershell Links
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916912106045440 |
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| author | Cetin, Selen Gecgel Ovatman, Tolga Kurt, Gunes Karabulut |
| author_facet | Cetin, Selen Gecgel Ovatman, Tolga Kurt, Gunes Karabulut |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16314 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cyber Physical Awareness via Intent-Driven Threat Assessment: Enhanced Space Networks with Intershell Links Cetin, Selen Gecgel Ovatman, Tolga Kurt, Gunes Karabulut Machine Learning Artificial Intelligence Emerging Technologies 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. |
| title | Cyber Physical Awareness via Intent-Driven Threat Assessment: Enhanced Space Networks with Intershell Links |
| topic | Machine Learning Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2508.16314 |