Cross-Layer Detection of Wireless Misbehavior Using 5G RAN Telemetry and Operational Metadata

• URL: http://arxiv.org/abs/2511.21803v1
• Date: Wed, 26 Nov 2025 18:53:26 GMT
• Title: Cross-Layer Detection of Wireless Misbehavior Using 5G RAN Telemetry and Operational Metadata
• Abstract summary: We study how manipulation affects the coherence among physical layer measurements, MAC scheduling decisions, and configuration metadata.<n>Every manipulation produces a distinct and reproducible signature that is not visible from any single telemetry source.<n>Findings indicate that cross layer coherence provides a practical signal for detecting uplink misbehavior using only standard gNB telemetry.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 5G Standalone deployments can exhibit uplink misbehavior from user equipment that remains fully compliant with standard control plane procedures. Manipulations such as transmit power inflation, gradual timing drift, and short off grant bursts leave the signaling state intact but distort the expected relationships among the telemetry streams produced by the gNB. This work examines whether these cross layer relationships can serve as a reliable basis for identifying such misbehavior without introducing new signaling. Using a controlled 5G Standalone testbed with commercial user equipment and a software defined radio adversarial device, we study how each manipulation affects the coherence among physical layer measurements, MAC scheduling decisions, and configuration metadata. The results show that every manipulation produces a distinct and reproducible signature that is not visible from any single telemetry source. Power offsets weaken the natural connection between SNR and CQI, timing drift breaks the alignment maintained by the scheduler, and off grant activity produces uplink energy that does not agree with allocation logs. These inconsistencies appear in merged multi layer time series traces and in cross domain views such as the SNR to CQI plane. The findings indicate that cross layer coherence provides a practical signal for detecting uplink misbehavior using only standard gNB telemetry, with no protocol modifications required, which makes the method suitable for integration into operational monitoring and auditing systems.

Related papers

• S$^3$AM: A Single-Stream SAM with Reliability-Calibrated Frequency Adapter for Multi-modal Salient Object Detection [74.15663758681849]
  Single-stream framework integrates reliability-calibrated frequency adaptation into the adopted SAM backbone for MSOD.<n>Experiments on RGB-D, RGB-T, and RGB-NIR salient object detection benchmarks validate that the proposed framework achieves competitive performance.
  arXiv  Detail & Related papers  (2026-08-18T08:05:00Z)
• What Accuracy and Gradient Cosine Miss: Evaluating Feedback Alignment via Scale Stability, Reference Validity, and Depth Utility [48.10132234701036]
  We propose a diagnostic evaluation protocol based on three checks -- scale stability, reference validity, and depth utility -- together with per-layer rather than aggregate cosine reporting.<n>Across multiple architectures and methods, our protocol identifies all failures with wide calibration margins.
  arXiv  Detail & Related papers  (2026-06-19T06:04:17Z)
• Robust and Precise Application Fingerprinting on 5G Physical Uplink Channel [14.703722937793641]
  Air fingerprinting infers application activity by sniffing metadata from cellular control channels.<n>5G encrypts these channels, breaking the attack chain that prior attacks depend on.<n>This paper reveals a physical-layer side channel that bypasses encryption.
  arXiv  Detail & Related papers  (2026-06-13T09:36:34Z)
• SCOPE: A Syndrome-Driven Control Plane for QEC-Enabled Quantum Networks [7.746657732894142]
  We present SCOPE, a network-layer architecture that enables joint routing and coding optimization using purely passive telemetry.<n>SCOPE reduces estimation error by more than 60% relative to a standard EM baseline.<n>In large-scale networks, this precision reduces logical error rates by 30-35% (up to 65%) against topology-aware baselines.
  arXiv  Detail & Related papers  (2026-06-07T23:00:09Z)
• WISV: Wireless-Informed Semantic Verification for Distributed Speculative Decoding in Device-Edge LLM Inference [56.297697169678095]
  WISV (Wireless-Informed Semantic Verification) is a novel distributed speculative decoding framework.<n>We show that WISV achieves up to a 60.8% increase in accepted length, a 37.3% reduction in interaction rounds, and a 31.4% improvement in end-to-end latency.<n>We validate WISV on a hardware testbed comprising an NVIDIA Jetson AGX Orin and an A40-equipped server.
  arXiv  Detail & Related papers  (2026-04-20T01:29:56Z)
• Benchmarking Dataset for Presence-Only Passive Reconnaissance in Wireless Smart-Grid Communications [2.721477719641864]
  This paper introduces an IEEE-inspired, literature-anchored benchmark dataset generator for passive reconnaissance over a tiered Home Area Network (HAN), Neighborhood Area Network (NAN), and Wide Area Network (WAN) communication graph with heterogeneous wireless and wireline links.
  arXiv  Detail & Related papers  (2026-03-10T12:39:03Z)
• Generalizing GNNs with Tokenized Mixture of Experts [75.8310720413187]
  We show that improving stability requires reducing reliance on shift-sensitive features, leaving an irreducible worst-case generalization floor.<n>We propose STEM-GNN, a pretrain-then-finetune framework with a mixture-of-experts encoder for diverse computation paths.<n>Across nine node, link, and graph benchmarks, STEM-GNN achieves a stronger three-way balance, improving robustness to degree/homophily shifts and to feature/edge corruptions while remaining competitive on clean graphs.
  arXiv  Detail & Related papers  (2026-02-09T22:48:30Z)
• Learning to Separate RF Signals Under Uncertainty: Detect-Then-Separate vs. Unified Joint Models [53.79667447811139]
  We show that a single deep neural architecture learns to jointly detect and separate when applied directly to the received signal.<n>These findings highlight UJM as a scalable and practical alternative to DTS, while opening new directions for unified separation under broader estimation.
  arXiv  Detail & Related papers  (2026-02-04T15:25:02Z)
• Unsupervised Detection of Spatiotemporal Anomalies in PMU Data Using Transformer-Based BiGAN [0.0]
  We introduce T-BiGAN, a framework that window-attention Transformers within a bidirectional Generative Adversarial Network (BiGAN)<n>Its encoderdecoder captures architecture while discriminator enforces cycle consistency to align latent space with the true data distribution.<n>Anomalies are flagged in real-time using an adaptive score that combines reconstruction error, latent space drift, and discriminator confidence.
  arXiv  Detail & Related papers  (2025-09-30T00:16:35Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• Adaptive t Design Dummy-Gate Obfuscation for Cryogenic Scale Enforcement [0.0]
  Cloud quantum services can reveal circuit structure and timing through scheduler metadata, latency patterns, and co-tenant interference.<n>We introduce NADGO, a scheduling and obfuscation stack that enforces operational privacy for gate-model workloads.<n>We prototype the approach on a 4-qubit superconducting tile with cryo-CMOS control and evaluate both depth-varied local-random circuits and small QAOA instances.
  arXiv  Detail & Related papers  (2025-08-31T12:12:48Z)
• WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales [22.789611187514975]
  Methods for nonparametric sequential testing -- especially conformal test martingales (CTMs) and anytime-valid inference -- offer promising tools for this monitoring task.<n>Existing approaches are restricted to monitoring limited hypothesis classes or alarm criteria''
  arXiv  Detail & Related papers  (2025-05-07T17:53:47Z)
• Perimeter Control with Heterogeneous Metering Rates for Cordon Signals: A Physics-Regularized Multi-Agent Reinforcement Learning Approach [12.86346901414289]
  Perimeter Control (PC) strategies have been proposed to address urban road network control in oversaturated situations. This paper leverages a Multi-Agent Reinforcement Learning (MARL)-based traffic signal control framework to decompose this PC problem. A physics regularization approach for the MARL framework is proposed to ensure the distributed cordon signal controllers are aware of the global network state.
  arXiv  Detail & Related papers  (2023-08-24T13:51:16Z)
• Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects [78.84264189471936]
  NextG communication systems are beginning to explore shifting this design paradigm to reliably executing a given task such as in task-oriented communications. Wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label. Task-oriented communications is considered by jointly training the transmitter, receiver and classifier functionalities as an encoder-decoder pair for the edge device and the gNodeB.
  arXiv  Detail & Related papers  (2022-12-19T17:54:36Z)
• Time-to-Green predictions for fully-actuated signal control systems with supervised learning [56.66331540599836]
  This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
  arXiv  Detail & Related papers  (2022-08-24T07:50:43Z)
• Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control [37.10638636086814]
  We consider a joint uplink and downlink scheduling problem of a fully distributed wireless control system (WNCS) with a limited number of frequency channels.<n>We develop a deep reinforcement learning (DRL) based framework for solving it.<n>To tackle the challenges of a large action space in DRL, we propose novel action space reduction and action embedding methods.
  arXiv  Detail & Related papers  (2021-09-26T11:27:12Z)
• Fault-tolerant parity readout on a shuttling-based trapped-ion quantum computer [64.47265213752996]
  We experimentally demonstrate a fault-tolerant weight-4 parity check measurement scheme. We achieve a flag-conditioned parity measurement single-shot fidelity of 93.2(2)%. The scheme is an essential building block in a broad class of stabilizer quantum error correction protocols.
  arXiv  Detail & Related papers  (2021-07-13T20:08:04Z)
• Noisy receivers for quantum illumination [0.0]
  Quantum illumination (QI) promises unprecedented performances in target detection. Signal and idler recombination forms a crucial barrier to the protocol's success. This could potentially be mitigated if performing a measurement on the idler mode could still yield a quantum advantage.
  arXiv  Detail & Related papers  (2020-06-08T17:42:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.