2019
DOI: 10.1364/jocn.11.00a209
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Monitoring and Physical-Layer Attack Mitigation in SDN-Controlled Quantum Key Distribution Networks

Abstract: Quantum Key Distribution (QKD) has been identified as a secure method for providing symmetric keys between two parties based on the fundamental laws of quantum physics, making impossible for a third party to copy the quantum states exchanged without being detected by the sender (Alice) and receiver (BoB) and without altering the original states. However, when QKD is applied in a deployed optical network, physical layer intrusions may occur in the optical links by injecting harmful signals directly into the opt… Show more

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Cited by 32 publications
(18 citation statements)
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“…The SDN-based experiment of monitoring and mitigation of physical layer attacks was reported [162]. Real-time monitoring of QBER and the secret key rate was used to recalculate routes for the quantum channel establishment.…”
Section: Qkd Software Defined Networkingmentioning
confidence: 99%
“…The SDN-based experiment of monitoring and mitigation of physical layer attacks was reported [162]. Real-time monitoring of QBER and the secret key rate was used to recalculate routes for the quantum channel establishment.…”
Section: Qkd Software Defined Networkingmentioning
confidence: 99%
“…Planning of protection resources capable of guaranteeing protection from jamming attacks based on identifying overlaps in attack scopes of the working and the backup path of each connection was presented in [35]. In [36], the authors experimentally demonstrated mitigation of attacks in Quantum Key Distribution (QKD)-enabled optical networks under physical-layer attacks aimed at key-rate degradation.…”
Section: B Optical Network Security Managementmentioning
confidence: 99%
“…Other examples of how the SDN and network function virtualization (NFV) paradigms-widely adopted in classical networks-can be extended to quantum networks, including internet of Things (IoT) [54] and 5G [55] applications, are reported in [55][56][57][58][59][60][61][62][63], further proving the importance of moving from current point-to-point setups to more complex topologies. Furthermore, recent advances in machine learning (ML) can greatly help to improve the automation of QKD systems, whose performance depends of many parameters that must be finely tuned, thereby significantly decreasing operational and maintenance costs.…”
Section: Qkd Networkingmentioning
confidence: 99%