PaperlayerView exact revision on arXiv

Source-linked AI summary

Quantum-Based Solutions for Security Enhancement in Open Radio Access Networks

Dzung Quoc Ngo, Tharmikka Raveendranathan, Tuan Anh Le, Vinod Sharma, Purav Shah, Huan X. Nguyen

arXiv:2608.28480v1cs.CRcs.ET

TL;DR

O-RAN’s disaggregated architecture expands its attack surface, while quantum computing threatens conventional public-key cryptography and long-term confidentiality. The paper presents a Zero Trust framework integrating PQC, quantum protocols, and quantum intelligence through RIC applications and dedicated quantum nodes. It concludes that PQC should provide the interface baseline, with QKD offering defense in depth on suitable optical paths, while quantum-native deployment remains constrained by hardware and integration requirements.

  • Problem

    O-RAN’s open, multi-vendor architecture creates new vulnerabilities, and quantum adversaries threaten conventional cryptographic mechanisms used for authentication, key exchange, and signatures.

  • Method

    The paper maps O-RAN threats to quantum-safe mechanisms within Zero Trust and proposes deployment through RIC applications, dedicated quantum nodes, PQC, QKD, and quantum machine learning.

  • Results

    PQC is positioned as the baseline for O-RAN interfaces, while QKD is a defense-in-depth option for stable, high-value optical paths.

  • Takeaways & Limitations

    Secure future O-RAN architectures should combine quantum-resilient software, Zero Trust enforcement, and selectively deployed quantum nodes while preserving openness and scalability.

Abstract

from arXiv · show

Open Radio Access Networks (O-RAN) introduce unprecedented flexibility, interoperability, and intelligence into next-generation wireless systems, but their disaggregated and software-defined architecture also expands the attack surface and creates new security vulnerabilities. Conventional cryptographic mechanisms, while effective against classical threats, may become insufficient in the presence of quantum-enabled adversaries. This article presents a comprehensive perspective on quantum security for O-RAN, examining how quantum-resilient mechanisms can enhance confidentiality, authentication, and trust across the RAN ecosystem. It discusses post-quantum cryptography (PQC), quantum cryptography, quantum authentication, and quantum-enhanced threat detection within a zero-trust architecture based on continuous verification, least privilege, and micro-segmentation. Their integration with the Near-Real-Time (Near-RT) RAN Intelligent Controller, O-Cloud, and open interfaces is analyzed, together with practical deployment considerations, technology maturity, and adoption timelines. Finally, open research directions are outlined toward secure, resilient, and future-proof O-RAN architectures for 6G networks.

I. Introduction

O-RAN’s open, disaggregated architecture expands flexibility and interoperability while creating a broader, harder-to-secure attack surface. The paper frames quantum-resilient mechanisms within Zero Trust as a foundation for secure, scalable, future-proof O-RAN.

  • O-RAN decomposes the base station into RU, DU, and CU connected through standardized open interfaces, enabling multi-vendor interoperability.
  • The disaggregated architecture and third-party applications expand vulnerabilities across control, management, and user planes.Reported threats include unauthorized access, signaling storms, MITM attacks, and adversarial manipulation of AI/ML pipelines.
  • Quantum adversaries threaten RSA and ECC through Shor’s algorithm, while harvest-now-decrypt-later attacks create long-term risks for telecommunications data.The risk is especially significant because telecommunications systems have extended operational life cycles.
  • The framework integrates PQC, quantum cryptography, quantum authentication, and quantum-enhanced intelligence into O-RAN security.The proposed mechanisms target confidentiality, authentication, key establishment, and adaptive threat detection.
  • The paper maps O-RAN vulnerabilities to quantum-safe defenses and proposes deployable enhancements that preserve openness and scalability.

II. Security concerns with O-RAN

O-RAN’s open interfaces, cloud-native software, and multi-vendor composition create security risks spanning physical access, authentication, authorization, and insider activity. Fine-grained protection is required because components communicate across standardized interfaces without a unified security perimeter.

  • O-RAN’s openness and decoupled architecture introduce multi-vendor insider threats and weaken the end-to-end security available in conventional RAN.O-RAN WG11 addresses requirements, threats, and mitigations for nodes, interfaces, and cloud-native deployment.
  • Attackers can exploit management-interface vulnerabilities or unauthorized fronthaul devices to flood physical interfaces and cause system crashes.
  • Every connection among O-RAN’s disaggregated components requires robust authentication and fine-grained authorization.Authentication verifies identities, while authorization determines permissions and privileges.
  • Excessive xApp permissions can expose sensitive APIs and compromise the entire Near-RT RIC through elevation-of-privilege attacks.The affected environments include O1, E2, A1, the SMO, virtualization layers, and the RIC.

B. Privacy, Integrity, Confidentiality and Availability

O-RAN faces threats to privacy, integrity, confidentiality, and availability across its management, user, synchronization, and control planes. These include synchronization spoofing, RIC subscription abuse, signaling overload, and attacks on AI/ML models and applications.

  • MITM attacks can intercept fronthaul user, management, and synchronization communications, threatening all four core network-security pillars.
  • A malicious device can impersonate the PTP master clock, tamper with synchronization packets, and enable inaccurate synchronization and integrity violations.Fronthaul control-plane spoofing can also steal or modify uplink and downlink messages.
  • Malicious xApps can flood E2 subscription requests and redirect RMR-routed messages, compromising UE identification, tracking, priority, integrity, and confidentiality.
  • A hostile Near-RT RIC can exploit bidirectional A1 authentication to corrupt Non-RT RIC policies and potentially cause denial of service.
  • DoS attacks can collapse networks through synchronization failure, signaling storms, mass restarts, or excessive radio-resource consumption.
  • RIC AI/ML systems are exposed to poisoning, extraction, injection, model theft, logic corruption, and backdoor attacks.Third-party xApps and rApps add supply-chain risks, including privilege elevation, RIC compromise, and resource-exhausting fork bombs.

III. Quantum security solutions for O-RAN

Quantum security for O-RAN combines PQC, quantum communication, authentication, and machine learning with Zero Trust controls. PQC is positioned as the baseline for interfaces, while QKD provides defense in depth on suitable high-value optical paths and migration requires algorithm agility.

  • Candidate quantum solutions for O-RAN include PQC, quantum communication and cryptography, authentication, optimization, and quantum machine learning.These technologies are presented as mechanisms for addressing specific O-RAN threats.
  • Zero Trust evaluates the identities and security postures of O-RAN functions, workloads, and applications before authorizing requests at enforcement points.Continuous verification re-evaluates long-lived O1, O2, A1, and E2 sessions.
  • PQC provides quantum-resistant software upgrades on classical hardware, including ML-KEM for shared-secret establishment and ML-DSA for digital signatures.
  • PQC is the baseline for O-RAN interfaces, whereas QKD is a defense-in-depth option for stable, high-value optical paths.PQC supports routed-interface key establishment and signatures; QKD distributes symmetric keys but requires optical links and specialized key management.
  • Migration requires cryptographic inventory, algorithm agility, performance benchmarking, hybrid classical/PQC handshakes, downgrade protection, and certificate lifecycle planning.

C. Mutual Authentication and Authorization

The paper presents quantum identity authentication and quantum digital signatures as longer-term tools for authenticating parties, validating messages, and supporting continuous verification in O-RAN. Quantum secret sharing and multiparty computation extend authorization controls for multi-vendor collaboration, while classical threshold cryptography and PQC remain sooner-term alternatives.

  • Quantum authentication and signatures: Quantum identity authentication uses quantum states or entanglement-based challenge–response as evidence of identity, while quantum digital signatures support message authenticity and integrity.QDS also provides non-repudiation, but requires quantum-generated correlated information and specialized endpoints.
  • Quantum authentication and signatures: Integrating QIA into xApps can make them Quantum Authentication Servers and support continuous verification of UEs and internal network components.The approach combines service authentication and secured slicing for stricter access control.
  • Multi-party authorization: Quantum Secret Sharing divides a secret into shares so only an authorized threshold can reconstruct it, preventing any single participant or vendor from holding the complete secret.The concept is positioned for multi-party collaboration in multi-vendor O-RAN.
  • Multi-party authorization: Quantum Multiparty Computation enables joint computation without revealing private inputs and could support joint approval for exceptional configuration changes.This aligns with least-privilege and separation-of-duty policies.
  • Multi-party authorization: Classical threshold cryptography and PQC can provide similar governance sooner, while QSS and QMC remain candidates for future quantum-network trials.The comparison defines a nearer-term boundary for the longer-term quantum approaches.

D. Privacy Assurance, Confidentiality, and Integrity

The paper discusses QKD and QSDC as quantum communication mechanisms for protecting confidentiality and detecting interference, while emphasizing the management and deployment conditions required in multi-node O-RAN. Quantum protocols complement, rather than replace, Zero Trust policy enforcement.

  • Quantum key protection: QKD protects shared secret keys through quantum measurement and, in some protocols, entanglement, with eavesdropping detectable through measurement disturbance.Protocols include prepare-and-measure and entanglement-based approaches using discrete or continuous variables.
  • Quantum key protection: QKD deployment in multi-node O-RAN requires a control and management layer, including controllers, network managers, and Quantum Key Servers for subsets of components.Independent key domains can support micro-segmentation, but QKD does not create segmentation by itself.
  • Quantum communication alternatives: QSDC communicates without first distributing a reusable secret key and detects interference through measurement disturbance during transmission.Its deployment complexity, limited distance, and low transfer rate make it unsuitable as a near-term replacement for PQC or QKD in O-RAN.
  • Trust and deployment boundaries: Quantum protocols may reduce reliance on implicit trust in multi-vendor O-RAN, but they do not replace policy enforcement.Perfectly secure quantum bit commitment and coin-flipping variants are impossible, motivating weaker variants with reduced cheat probabilities.

E. Threat Detection and Mitigation

The paper proposes near-real-time threat monitoring and intervention through O-RAN xApps, using quantum machine learning for intrusion and anomaly detection and quantum optimization for mitigation decisions. QKD can also be implemented as standalone quantum blocks rather than embedded xApps.

  • Threat monitoring: An intrusion-detection xApp can continuously monitor network traffic to detect and prevent suspicious activities and potential threats during ongoing operations.The framework complements continuous authentication and monitoring managed by xApps.
  • Quantum machine learning: Quantum machine learning applies supervised, unsupervised, and reinforcement learning to threat detection, leveraging expanded computational bases and parallel computation.Classical data is generally encoded into qubits before quantum circuit processing.
  • Deployment models: QKD can be deployed as standalone quantum blocks, offering a less restrictive alternative to embedding quantum nodes inside xApps.Standalone components use new nodes and interfaces, whereas xApp integration eases deployment and O-RAN compatibility.
  • Quantum machine learning: Quantum-based support vector machines, decision trees, and random forests can identify suspicious activity from known attack patterns, while quantum Gaussian mixture models support anomaly detection.The anomaly models represent cluster distributions as wave functions.
  • Optimization-based mitigation: Quantum optimization can frame signaling-storm mitigation as balancing malicious UEs blocked against legitimate UEs wrongly blocked.The objective retains network availability and functionality while minimizing exposure and risk.

F. Poisoning Attacks

The paper addresses poisoning attacks as threats to AI/ML training and proposes protecting live data transmission, monitoring xApps, preserving dataset integrity, and considering quantum reinforcement learning for adaptive defense.

  • Attack characteristics: Poisoning attacks stealthily corrupt model parameters during training and can have widespread, long-lasting impacts compared with inference attacks.Because training data is targeted, mitigation emphasizes protecting the training process and its inputs.
  • Runtime protection: QKD and QSDC can protect live data transmission over the O1, A1, and E2 interfaces from attackers seeking to obtain or modify RIC data feeds.An integrated risk-assessment and threat-detection xApp can monitor other xApps for misbehavior from poisoning attacks or corrupted models.
  • Adaptive defense: Quantum reinforcement learning is proposed for enhancing and adapting threat models to new and potential attacks.Possible implementations include quantum-based Q-learning and experience replay.
  • Training-data protection: For offline-trained models, dataset integrity and privacy can be supported by blockchain, PQC, data masking, sanitization, QDS, QKD, and QSS/QMC.The listed mechanisms address authentication, auditing, security, and collaborative authorization needs.

IV. Integration of quantum nodes in O-RAN

Quantum capabilities can be integrated into O-RAN as xApps, enabling third-party network services for monitoring, control, authentication, key distribution, and quantum machine learning. However, practical deployment remains constrained by the need for advanced quantum hardware and transitional LOCC techniques.

  • Integration of quantum nodes in O-RAN: Quantum cryptography and related features require advanced hardware, while current quantum chips remain inadequate for fully realizing their capabilities.LOCC is presented as a transitional bridge between traditional and quantum information processing.
  • Integration of quantum nodes in O-RAN: Quantum authentication and key-distribution schemes can be integrated into O-RAN as xApps that provide third-party network services.xApps subscribe to decentralized RAN units, access live data, and make network adjustments.
  • Integration of quantum nodes in O-RAN: Quantum machine learning models can reside within xApps for network-wide monitoring and control.This placement supports compatibility with the O-RAN architecture for maintenance and testing by third-party vendors.

B. Dedicated Quantum Blocks in O-RAN

Dedicated quantum components provide a less restrictive alternative to embedding quantum functions in xApps, but they introduce security, testing, maintenance, and interoperability challenges. The paper therefore proposes phased adoption, beginning with PQC and extending toward hybrid and native quantum capabilities as hardware and standards mature.

  • Dedicated Quantum Blocks in O-RAN: Standalone quantum nodes can avoid predefined xApp interface restrictions, but their external interfaces may conflict with O-RAN maintenance and security requirements.The dedicated approach is presented as an alternative implementation for quantum key distribution.
  • Dedicated Quantum Blocks in O-RAN: QKD nodes generate and store secret keys for individual O-RAN components, while independent third-party QKD software operates outside the main network.External components require additional testing and monitoring to protect the core network from malicious insiders and outsiders.
  • C. Maturity and Implementation Roadmap: Short-term adoption prioritizes a 1–3 year PQC transition, including crypto-agile APIs, hybrid handshakes, software and model signing, and Zero Trust controls.ML-KEM and ML-DSA can run on classical O-Cloud and RIC infrastructure, but their larger cryptographic objects require platform benchmarking.
  • C. Maturity and Implementation Roadmap: Medium-term deployment combines broad PQC adoption with QKD pilots on selected high-value optical links as standards and product support stabilize.Pilots should measure key rates, loss, distance, consumption, failover, availability, trusted-node exposure, and tenant isolation; PQC fallback is required when quantum links fail.
  • C. Maturity and Implementation Roadmap: Long-term native quantum capabilities depend on scalable hardware, repeaters, and standardized quantum–classical interfaces, with planning horizons potentially extending beyond 7 years.These ranges are planning horizons rather than predictions of when a cryptographically relevant quantum computer will exist.

V. Conclusion

The paper presents a Zero Trust framework for integrating PQC, quantum authentication, QKD, and quantum machine learning into open, disaggregated O-RAN architectures. It identifies a PQC-first pathway for near-term progress while recognizing that current quantum hardware limits full deployment.

  • V. Conclusion: The framework maps O-RAN threats to quantum-resilient defenses and proposes deployment through intelligent-controller applications and standalone quantum nodes.The covered mechanisms include PQC, quantum identity authentication, QKD, and quantum machine learning.
  • V. Conclusion: A PQC-first roadmap offers a viable near-term pathway despite current quantum hardware limitations.The conclusion positions this transitional approach alongside longer-term quantum technology maturation and 6G standardization.
Loading 2608.28480v1…