Quantum-Augmented AI/ML for O-RAN: Hierarchical Threat Detection with Synergistic Intelligence and Interpretability (Technical Report)

2025-12-12

AI Summary

O-RAN’s modular, disaggregated architecture enhances flexibility but significantly expands the attack surface across control, user, and management planes. To address this, we propose the first quantum-enhanced collaborative threat detection framework tailored to O-RAN’s three-tier control plane—encompassing anomaly detection, intrusion confirmation, and multi-attack classification. Our method introduces a novel amplitude-and-entanglement joint feature encoding scheme, ensuring both high diagnostic accuracy and model interpretability; it further supports slice-aware, near-real-time RIC deployment. The framework integrates hybrid quantum-classical computation, latent-space geometric analysis, and probabilistic boundary assessment. Evaluated on synthetic and real-world O-RAN telemetry datasets, it achieves near-perfect accuracy (>99.8%), high recall, and markedly improved inter-class separability. Comprehensive multi-dimensional validation confirms its strong robustness and intrinsic interpretability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Detects cybersecurity threats in Open Radio Access Networks
• Integrates quantum computing with machine learning for threat detection
• Ensures interpretability and robustness in multi-layer defense framework

Innovation

• Hierarchical defense framework with three coordinated layers
• Integrates hybrid quantum computing and machine learning
• Uses amplitude- and entanglement-based feature encodings