arXiv subjects

Ingrid Huso

Publications and source records attributed to Ingrid Huso.

3 recordsLinked to original sources

arXiv2026

Cloudy Skies: Early Detection of Jamming Against Iridium LEO Satellite-IoT Links

Low Earth Orbit (LEO) satellite-IoT systems increasingly provide connectivity to mobile assets in remote areas, which makes their resilience to radio-frequency jamming a growing concern. Timely jamming detection, i.e., before complete link failure, has been studied for terrestrial networks and Global Navigation Satellite Systems (GNSS), but it remains unclear whether these approaches are effective on LEO links, where propagation varies rapidly, the received signal is weak, and jamming should be detected from the messages that are still correctly received. This paper investigates the extent to which jamming of Iridium LEO satellite-IoT links can be detected from the physical layer of correctly decoded messages. To this aim, we collect and publicly release a real-world dataset of Iridium messages under 47 jamming conditions, spanning two propagation environments, static and mobile receivers, and different jammer distances, powers, and signal types. We use this dataset to analyze three one-class autoencoder-based anomaly detectors operating on physical-layer information and compare them against two signal-quality estimators commonly used as jamming indicators in GNSS-based applications. Our results show that detectability is governed by the rise of the receiver noise floor, through which jammer power, distance, and receiver mobility act. With 1000-sample chunks, i.e., 40 ms of received signal at 25,000 samples per second (about four correctly decoded messages), the autoencoders reach an AUC of at least 0.84 whenever a noise jammer raises the noise floor by at least 9 dB, whereas the $C/N_0$ and SNR estimators fail, since received messages, although correctly decoded, retain a high SNR under jamming. Therefore, timely jamming detection for LEO satellite-IoT links like Iridium should rely on physical-layer information beyond the signal-quality metrics adopted in the GNSS domain.

cs.CR↗

arXiv2026

Where You Tap Matters: A Probe-and-Model Benchmark for Open-Set RF Fingerprinting

Radio Frequency Fingerprint Identification (RFFI) enables transmitter identification at the physical layer by learning device-specific impairments from received signals, yet the literature is inconsistent about where in the receiver chain those samples should be collected. Since distinct transformations are applied to the signal by the different receiver operations, i.e., carrier recovery, gain normalization, pulse shaping, and timing recovery, they can either tighten within-transmitter variability or suppress the features RFFI requires for classification. We present a systematic real-world evaluation of open-set, reconstruction-error RFFI using data collected at five probe points along a standard BPSK receiver chain. Our results show that RFFI is strongly probe-dependent: timing recovery and, to a lesser extent, carrier recovery enable low false-acceptance operation with limited in-distribution-out-of-distribution overlap, whereas other stages often require a false-acceptance ratio above 0.1 to achieve a true-acceptance ratio of 0.9. To test the validity of our findings across model selection, we benchmark several LLM-designed autoencoders using a controlled pipeline that holds preprocessing and MSE scoring fixed. These architectures confirm that RFFI is probe-dependent. Moreover, they do not outperform the baseline at the chosen operating point and typically increase training time. Overall, probe selection dominates reconstruction-based open-set RFFI performance, more than the autoencoder complexity.

cs.CR↗

arXiv2026

The Chronicles of Radio Frequency Fingerprinting

Radio Frequency Fingerprinting (RFF) has evolved from an early idea for radar emitter identification into a broad research field for wireless device identification and spectrum monitoring for security. Rather than presenting a conventional literature survey, this work provides a critical historical analysis of RFF organized around the field's major conceptual paradigm shifts from 1993 to 2026. We discuss the evolution of RFF across its fundamental methodological phases, beginning with early transient-based approaches, in which transmitter turn-on behavior, unintentional modulation, and hardware nonlinearities were treated as the primary fingerprint sources. We then examine the transition to digital communications, during which attention shifted to steady-state impairments and to engineered features extracted from signals. Next, we discuss the Machine Learning period, which standardized the RFF workflow around feature extraction, dimensionality reduction, and supervised classification, followed by the Deep Learning period, in which representation learning from raw IQ samples significantly improved performance and expanded the application space. Beyond a chronological list of methods and best practices, this paper critically examines the changing assumptions and persistent limitations that have driven these transitions. We highlight the central challenges that continue to shape the field, including channel dependence, receiver sensitivity, limited dataset realism, poor cross-domain generalization, open-set recognition, and adversarial robustness. By organizing more than three decades of work into a coherent narrative, this paper clarifies the evolution of RFF, identifies persistent limitations, and outlines the key research directions required to move the field toward dependable real-world adoption.

cs.CR↗