Document Type

Article

Publication Date

2025

DOI

10.36227/techrxiv.176594693.32723934/v1

Publication Title

TechRxiv

Pages

1-14

Abstract

Open Radio Access Networks (O-RAN) enhance modularity and telemetry granularity but also widen the cybersecurity attack surface across disaggregated control, user and management planes. We propose a hierarchical defense framework with three coordinated layers—anomaly detection, intrusion confirmation, and multiattack classification—each aligned with O-RAN’s telemetry stack. Our approach integrates hybrid quantum computing and machine learning, leveraging amplitude- and entanglement-based feature encodings with deep and ensemble classifiers. We conduct extensive benchmarking across synthetic and real-world telemetry, evaluating encoding depth, architectural variants, and diagnostic fidelity. The framework consistently achieves near-perfect accuracy, high recall, and strong class separability. Multi-faceted evaluation across decision boundaries, probabilistic margins, and latent space geometry confirms its interpretability, robustness, and readiness for slice-aware diagnostics and scalable deployment in near-RT and non-RT RIC domains.

Rights

© 2025 The Authors.

Published under the terms of a Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Comments

e-Prints posted on TechRxiv are preliminary reports that are not peer reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in the media as established information.

Original Publication Citation

Le, T., Le, V., & Shetty, S. (2025). Quantum-augmented AI/ML for O-RAN: Hierarchical threat detection with synergistic intelligence and interpretability. TechRxiv. https://doi.org/10.36227/techrxiv.176594693.32723934/v1

ORCID

0000-0002-8789-0610 (Shetty)

Download

Share

COinS