StormShield fingerprints and blocks malicious UEs in O-RAN 5G RANs to prevent RRC signaling storms, achieving 97.6% detection accuracy within 106.5 ms on an OTA testbed with OAI and commercial hardware.
An Unsupervised Graph Neural Network Approach to Deceive UAV Network Reconnaissance Attack,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
LatencyScope models 5G RAN latency sources across the protocol stack and provides a configuration analyzer that identifies settings meeting latency-reliability targets, validated on open-source testbeds and commercial network measurements where it outperforms prior models and simulators.
GraphSAGE achieves 94.2% detection rate, 0.955 AUC, and 1.4s response time for cyberattacks on drone systems in an emulation study, outperforming GCN and GAT.
citing papers explorer
-
StormShield: Fingerprint-Based Detection and Mitigation of RRC Signaling Storms in O-RAN 5G RANs
StormShield fingerprints and blocks malicious UEs in O-RAN 5G RANs to prevent RRC signaling storms, achieving 97.6% detection accuracy within 106.5 ms on an OTA testbed with OAI and commercial hardware.
-
LatencyScope: A System-Level Mathematical Framework for 5G RAN Latency
LatencyScope models 5G RAN latency sources across the protocol stack and provides a configuration analyzer that identifies settings meeting latency-reliability targets, validated on open-source testbeds and commercial network measurements where it outperforms prior models and simulators.