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cs.CRcs.LGquant-ph
Local ID: 2605.19233v2
AI Summary: gemma4:e4b
Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets
By Carlos A. Durán Paredes, Javier E. León Calderón, Nicolás Sánchez Perea, Germán Darío Díaz, Camilo Segura Quintero
Revision History Timeline
v15/19/2026
5/19/2026
“10 pages, 7 figures, 1 table; open Qiskit 2.x implementation available at https://github.com/Carlosandp/TLM-UAV-Quantum-Anomaly-Detection”
v25/28/2026
5/28/2026
“10 pages, 7 figures, 1 table; open Qiskit 2.x implementation available at https://github.com/Carlosandp/TLM-UAV-Quantum-Anomaly-Detection”
★ Version indexed in ExplorerComparing v1 vs v2
Green = Added • Red = Removed
Title Comparison
Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets
Authors Comparison
Removed:German Darío Díaz
Added:Germán Darío Díaz
Unchanged:Carlos A. Durán Paredes, Javier E. León Calderón, Nicolás Sánchez Perea, Camilo Segura Quintero
v1 Comment
“10 pages, 7 figures, 1 table; open Qiskit 2.x implementation available at https://github.com/Carlosandp/TLM-UAV-Quantum-Anomaly-Detection”
v2 Comment
“10 pages, 7 figures, 1 table; open Qiskit 2.x implementation available at https://github.com/Carlosandp/TLM-UAV-Quantum-Anomaly-Detection”
Abstract Word Diff
Unmanned aerial vehicles (UAVs) are cyber-physical systems whose attack surface spans networked avionics and on-board sensor fusion: a compromised GPS or battery module can mimic a benign mission segment and evade naive anomaly detectors. We present a leakage-free evaluation of quantum machine learning for UAV anomaly detection on the multi-sensor TLM:UAV benchmark. Three contributions support the study. (i) A group-aware temporal protocol (B2) partitions the dataset into ten contiguous TimeUS blocks and evaluates over ten seeds, eliminating the inflation produced by random stratified splits that mix neighbouring samples. (ii) A three-mode feature audit (full/loose/strict) quantifies how much accuracy stems from instantaneous physical signals versus contextual proxies (cumulative energy, battery state, GPS trajectory). (iii) A hybrid XGBoost + Data Reuploading (DRU) classifier is benchmarked against five paired non-linear controls (raw, PCA, polynomial-2, random-RBF, and an untrained DRU map) under identical budgets. The standalone DRU does not consistently match the strongest classical baseline across seeds; however, the trained-DRU hybrid is the only model whose mean F1 macro shifts upward from full to strict (+0.05), a directional signal that the per-seed standard deviations prevent from being interpreted as a statistically established difference. The trained-DRU hybrid also records the lowest mean false-alarm rate under proxy-free evaluation, subject to the inter-seed variance reported. We frame this as an incremental, reproducible quantum-enhanced hybrid benefit, and provide an open Qiskit 2.x implementation as a benchmark for cybersecurity analytics in NISQ-era aerospace systems.