  Integrated sensing and communication (ISAC) is a novel paradigm using
crowdsensing spectrum sensors to help with the management of spectrum scarcity.
However, well-known vulnerabilities of resource-constrained spectrum sensors
and the possibility of being manipulated by users with physical access
complicate their protection against spectrum sensing data falsification (SSDF)
attacks. Most recent literature suggests using behavioral fingerprinting and
Machine/Deep Learning (ML/DL) for improving similar cybersecurity issues.
Nevertheless, the applicability of these techniques in resource-constrained
devices, the impact of attacks affecting spectrum data integrity, and the
performance and scalability of models suitable for heterogeneous sensors types
are still open challenges. To improve limitations, this work presents seven
SSDF attacks affecting spectrum sensors and introduces CyberSpec, an
ML/DL-oriented framework using device behavioral fingerprinting to detect
anomalies produced by SSDF attacks affecting resource-constrained spectrum
sensors. CyberSpec has been implemented and validated in ElectroSense, a real
crowdsensing RF monitoring platform where several configurations of the
proposed SSDF attacks have been executed in different sensors. A pool of
experiments with different unsupervised ML/DL-based models has demonstrated the
suitability of CyberSpec detecting the previous attacks within an acceptable
timeframe.
