QML-PipeGuard is a framework for runtime behavioral fingerprinting of QML pipelines that absorbs benign drift while detecting adversarial channel substitution via informationally complete measurements.
Security intrusion detection using quantum machine learning techniques
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Hybrid XGBoost plus data-reuploading quantum model shows modest F1 gain and lowest false-alarm rate in proxy-free evaluation on temporally partitioned TLM:UAV data, framed as incremental NISQ-era benefit.
citing papers explorer
-
QML-PipeGuard: Drift-Aware Behavioral Fingerprinting for Quantum Machine Learning Pipeline Integrity
QML-PipeGuard is a framework for runtime behavioral fingerprinting of QML pipelines that absorbs benign drift while detecting adversarial channel substitution via informationally complete measurements.
-
Quantum Machine Learning for Cyber-Physical Anomaly Detection in Unmanned Aerial Vehicles: A Leakage-Free Evaluation with Proxy-Audited Feature Sets
Hybrid XGBoost plus data-reuploading quantum model shows modest F1 gain and lowest false-alarm rate in proxy-free evaluation on temporally partitioned TLM:UAV data, framed as incremental NISQ-era benefit.