The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding
arXiv:2607.03411v1 Announce Type: cross Abstract: Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability, integrity, and trust. Robust interference monitoring (i.e., detection, classification, characterization, and direction finding) is therefore essential to identify and localize anomalous signals. While machine learning (ML) promises improved performance in complex environments, its development and validation depend on large-scale datasets that capture realistic signal and channel variability. Colle
The increasing reliance on wireless communications and satellite navigation, coupled with escalating geopolitical tensions, makes robust interference detection and mitigation a critical and immediate security concern.
This dataset provides essential tools for developing and validating advanced machine learning techniques to counter jamming and spoofing, which are direct threats to both civilian infrastructure and military operations.
The availability of a publicly available, dynamic interference dataset simulated with Sionna will accelerate ML-driven solutions for interference monitoring, improving the resilience of RF-dependent systems.
- · Defence contractors
- · Satellite operators
- · RF cybersecurity firms
- · Machine learning researchers
- · Adversaries relying on electronic warfare
- · Low-security wireless communication protocols
Improved detection and characterization of malicious RF signals become more feasible through advanced ML.
Enhanced resilience and trustworthiness of critical infrastructure reliant on wireless communications and GPS.
Deters electronic warfare attacks by increasing the cost and decreasing the effectiveness of jamming and spoofing operations.
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Read at arXiv cs.AI