Dynamic Orbital Resource Management (DORM) for 6G Networks: Enhancing Cybersecurity in Non-Terrestrial Systems
The integration of Non-Terrestrial Networks (NTNs) into 6G systems promises seamless global connectivity but also introduces unprecedented cybersecurity challenges. Due to their wide coverage area, satellite mobility, and open communication channels, NTNs are uniquely vulnerable to jamming, spoofing...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11083558/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The integration of Non-Terrestrial Networks (NTNs) into 6G systems promises seamless global connectivity but also introduces unprecedented cybersecurity challenges. Due to their wide coverage area, satellite mobility, and open communication channels, NTNs are uniquely vulnerable to jamming, spoofing, and eavesdropping attacks. Their limited physical security further exposes them to cyber-physical threats, including advanced persistent threats (APTs) targeting satellite control systems and denial-of-service (DoS) attacks that can degrade services critical to autonomous vehicles and positioning systems. To address these issues, this paper introduces the Dynamic Orbital Resource Management (DORM) framework—a machine learning-driven system for cybersecurity threat detection and resource optimization in 6G NTNs. DORM leverages predictive modeling and real-time monitoring to detect anomalies such as jamming, spoofing, and DoS attacks while simultaneously optimizing network performance. It processes over 10,000 network events across various orbital configurations to learn threat patterns and resource usage behaviors. Simulation results show that DORM achieves 96.8% overall accuracy in detecting threats, with specific detection rates of 96.2% for jamming, 94.8% for spoofing, and 93.9% for DoS attacks. Even under high-intensity attack scenarios, the framework maintains 91.4% detection accuracy and 78.7% system utility. Compared to existing methods, DORM reduces latency by 35.7% (from 85.6 ms to 30.2 ms) and interference by 28.9% (from 8.1 dB to 2.5 dB), while preserving 92.1% overall system utility in standard conditions. These results demonstrate DORM’s potential to deliver both security and performance in dynamic and adversarial NTN environments. The proposed framework lays the groundwork for building resilient, adaptive, and secure 6G NTNs, offering a significant leap forward in protecting global communication infrastructures against emerging threats. |
|---|---|
| ISSN: | 2169-3536 |