Enterprise AI Analysis
Quantum-Augmented AI/ML for O-RAN: Hierarchical Threat Detection with Synergistic Intelligence and Interpretability
This paper introduces a hierarchical cybersecurity framework for O-RAN, integrating quantum-inspired feature encoding with hybrid model architectures. It supports slice-aware diagnostics, telemetry fusion, and forensic classification across three operational layers: anomaly detection, intrusion confirmation, and multiattack classification. The framework achieves near-perfect accuracy, high recall, and strong class separability across synthetic and real-world telemetry, demonstrating interpretability, robustness, and readiness for scalable deployment.
Quantified Impact for Your Enterprise
Our analysis reveals how integrating Quantum-Augmented AI/ML for O-RAN translates into significant improvements in security posture and operational efficiency.
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Overall Accuracy
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Threat Recall Rate
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Classification F1 Score
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Near-RT Detection Latency
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O-RAN Threat Coverage
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
Anomaly Detection (L1)
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Intrusion Confirmation (L2)
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Multiattack Classification (L3)
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Policy Enforcement
Our three-layered defense framework aligns with O-RAN's telemetry stack, enabling granular threat detection from early anomalies to multiattack classification.
0.967
AUC Score (QML-RF, L2)
Layer 2 QML-RF model achieves a high AUC for binary intrusion confirmation, validating threats with exceptional separability and low false positives.
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F1 Score (QML+RF, L1)
Amplitude-encoded quantum features achieve near-perfect classification fidelity for Layer 1 anomaly detection, yielding a perfect F1 score.
| Encoding Type | Layer | Key Benefits | Resource Implications |
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| Non-entangled | L1 |
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| Partially Entangled | L2 |
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| Fully Entangled | L3 |
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Different quantum encoding strategies offer varying trade-offs in feature separability, interpretability, and resource utilization across O-RAN layers.
Mitigating Signaling Floods in Near-RT RIC
Our Layer 1 anomaly detection, powered by quantum-inspired DQNN models, effectively identifies excessive RRCSetupRequest messages, a key indicator of signaling floods. The system achieves near-perfect recall (0.9937), preventing network congestion and maintaining slice availability in near-real-time. This early detection mechanism is crucial for O-RAN resilience.
Calculate Your Potential ROI
Estimate the economic impact of implementing advanced AI/ML solutions in your O-RAN infrastructure.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap
A phased approach ensures seamless integration and maximum impact for your O-RAN security solution.
Phase 1: Discovery & Assessment (Weeks 1-4)
Comprehensive analysis of your existing O-RAN architecture, telemetry sources, and cybersecurity posture. Identify key integration points and data requirements for quantum-augmented AI/ML models.
Phase 2: Model Customization & Training (Weeks 5-12)
Adaptation and fine-tuning of hierarchical DQNN models using your enterprise-specific telemetry. Quantum encoding strategies optimized for your O-RAN environment and threat landscape.
Phase 3: Pilot Deployment & Validation (Weeks 13-20)
Staged deployment of Layer 1 (Anomaly Detection) in a controlled Near-RT RIC environment. Rigorous validation against synthetic and real-world attack scenarios, ensuring high fidelity and low false positives.
Phase 4: Full-Scale Integration & Monitoring (Weeks 21+)
Deployment of Layers 2 (Intrusion Confirmation) and 3 (Multiattack Classification) across O-Cloud. Ongoing performance monitoring, adaptive model updates, and forensic traceability for continuous security enhancement.
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