# Quantum Key Distribution Protocol Selector Based on Machine Learning for Next-Generation Networks

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## Abstract

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## 1. Introduction

- A Deep Convolutional Neural Network method for determining appropriate QKD protocols with good accuracy in contrast to previous machine learning models is proposed.
- We show that the Tree-CNN architecture can present an improved performance with the Tanh Exponential Activation Function (TanhExp).
- The proposed QKD is tested in a scenario that takes into account three different quantum cryptography algorithms in order to achieve highly secure communication between the IoT devices and the controllers of an NGN, such as 6G.

## 2. Quantum Key Distributions

## 3. Security Problems in 6G Networks

## 4. Proposed Method

#### 4.1. Parameters for the QKD Selection

- ${D}_{0}$ = ${10}^{-10}$ to ${10}^{-5}$;
- $m{e}_{d}$ = 0.00 to 0.06;
- $pd$ = 0.1 to 0.9;
- $pn$ = ${10}^{6}$ to ${10}^{16}$;
- $TD$ = 1 to 600 km.

#### 4.2. Deep Convolutional Neural Network, Tree-CNN

#### 4.3. Evaluation Metrics

#### 4.4. IoT Scenario

## 5. Results and Discussions

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Tsai, C.W.; Yang, C.W.; Lin, J.; Chang, Y.C.; Chang, R.S. Quantum key distribution networks: Challenges and future research issues in security. Appl. Sci.
**2021**, 11, 3767. [Google Scholar] [CrossRef] - Naeem, M.A.; Zikria, Y.B.; Ali, R.; Tariq, U.; Meng, Y.; Bashir, A.K. Cache in fog computing design, concepts, contributions, and security issues in machine learning prospective. Digit. Commun. Netw. 2022; in press. [Google Scholar] [CrossRef]
- Nawaz, S.J.; Sharma, S.K.; Wyne, S.; Patwary, M.N.; Asaduzzaman, M. Quantum machine learning for 6G communication networks: State-of-the-art and vision for the future. IEEE Access
**2019**, 7, 46317–46350. [Google Scholar] [CrossRef] - Terra Vieira, S.; Lopes Rosa, R.; Zegarra Rodríguez, D.; Arjona Ramírez, M.; Saadi, M.; Wuttisittikulkij, L. Q-meter: Quality monitoring system for telecommunication services based on sentiment analysis using deep learning. Sensors
**2021**, 21, 1880. [Google Scholar] [CrossRef] - Zheng, Z.; Bashir, A.K. Graph-enabled intelligent vehicular network data processing. IEEE Trans. Intell. Transp. Syst.
**2022**, 23, 4726–4735. [Google Scholar] [CrossRef] - Affonso, E.T.; Rodríguez, D.Z.; Rosa, R.L.; Andrade, T.; Bressan, G. Voice quality assessment in mobile devices considering different fading models. In Proceedings of the 2016 IEEE International Symposium on Consumer Electronics (ISCE), Sao Paulo, Brazil, 28–30 September 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 21–22. [Google Scholar]
- Angara, P.P.; Stege, U.; MacLean, A.; Müller, H.A.; Markham, T. Teaching quantum computing to high-school-aged youth: A hands-on approach. IEEE Trans. Quantum Eng.
**2022**, 3, 21409089. [Google Scholar] [CrossRef] - Parthasarathy, R.; Bhowmik, R.T. Quantum optical convolutional neural network: A novel image recognition framework for quantum computing. IEEE Access
**2021**, 9, 103337–103346. [Google Scholar] [CrossRef] - Watrous, J. On one-dimensional quantum cellular automata. In Proceedings of the IEEE 36th Annual Foundations of Computer Science, Milwaukee, WI, USA, 23–25 October 1995; pp. 528–537. [Google Scholar] [CrossRef]
- Li, S.S.; Long, G.L.; Bai, F.S.; Feng, S.L.; Zheng, H.Z. Quantum computing. Proc. Natl. Acad. Sci. USA
**2001**, 98, 11847–11848. [Google Scholar] [CrossRef] [Green Version] - Wu, J.; Chen, Y.; Zhou, C.; Chen, Z.; Xu, C.; Song, L. A remote security computational ghost imaging method based on quantum key distribution technology. IEEE Access
**2022**, 10, 18899–18909. [Google Scholar] [CrossRef] - Kumar, A.; Bhatia, S.; Kaushik, K.; Gandhi, S.M.; Devi, S.G.; De Pacheco, J.D.A.; Mashat, A. Survey of promising technologies for quantum drones and networks. IEEE Access
**2021**, 9, 125868–125911. [Google Scholar] [CrossRef] - Harwood, S.; Gambella, C.; Trenev, D.; Simonetto, A.; Bernal, D.; Greenberg, D. Formulating and solving routing problems on quantum computers. IEEE Trans. Quantum Eng.
**2021**, 2, 3100118. [Google Scholar] [CrossRef] - Da Xu, L. Emerging enabling technologies for industry 4.0 and beyond. Inf. Syst. Front.
**2022**, 21, 1218. [Google Scholar] - Botsinis, P.; Alanis, D.; Babar, Z.; Nguyen, H.V.; Chandra, D.; Ng, S.X.; Hanzo, L. Quantum search algorithms for wireless communications. IEEE Commun. Surv. Tutorials
**2019**, 21, 1209–1242. [Google Scholar] [CrossRef] [Green Version] - Abd El-Latif, A.A.; Abd-El-Atty, B.; Venegas-Andraca, S.E.; Elwahsh, H.; Piran, M.J.; Bashir, A.K.; Song, O.Y.; Mazurczyk, W. Providing end-to-end security using quantum walks in IoT networks. IEEE Access
**2020**, 8, 92687–92696. [Google Scholar] [CrossRef] - Satoh, T.; Nagayama, S.; Suzuki, S.; Matsuo, T.; Hajdušek, M.; Meter, R.V. Attacking the quantum Internet. IEEE Trans. Quantum Eng.
**2021**, 2, 4102617. [Google Scholar] [CrossRef] - Arul, R.; Raja, G.; Almagrabi, A.O.; Alkatheiri, M.S.; Chauhdary, S.H.; Bashir, A.K. A quantum-safe key hierarchy and dynamic security association for LTE/SAE in 5G scenario. IEEE Trans. Ind. Inform.
**2019**, 16, 681–690. [Google Scholar] [CrossRef] [Green Version] - Mavroeidis, V.; Vishi, K.; Zych, M.D.; Jøsang, A. The impact of quantum computing on present cryptography. arXiv
**2018**, arXiv:1804.00200. [Google Scholar] [CrossRef] [Green Version] - Diffie, W.; Hellman, M. New directions in cryptography. IEEE Trans. Inf. Theory
**1976**, 22, 644–654. [Google Scholar] [CrossRef] [Green Version] - Sharbaf, M.S. Quantum cryptography: A new generation of information technology security system. In Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations, Las Vegas, NV, USA, 27–29 April 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 1644–1648. [Google Scholar]
- Su, H.Y. Simple analysis of security of the BB84 quantum key distribution protocol. Quantum Inf. Process.
**2020**, 19, 661. [Google Scholar] [CrossRef] - Ren, Z.A.; Chen, Y.P.; Liu, J.Y.; Ding, H.J.; Wang, Q. Implementation of machine learning in quantum key distributions. IEEE Commun. Lett.
**2020**, 25, 940–944. [Google Scholar] [CrossRef] - Lohachab, A.; Lohachab, A.; Jangra, A. A comprehensive survey of prominent cryptographic aspects for securing communication in post-quantum IoT networks. Internet Things
**2020**, 9, 100174. [Google Scholar] [CrossRef] - Fernández-Caramés, T.M. From pre-quantum to post-quantum IoT security: A survey on quantum-resistant cryptosystems for the Internet of Things. IEEE Internet Things J.
**2019**, 7, 6457–6480. [Google Scholar] [CrossRef] - Liu, X.; Di, X. TanhExp: A smooth activation function with high convergence speed for lightweight neural networks. IET Comput. Vis.
**2021**, 15, 136–150. [Google Scholar] [CrossRef] - Mendonça, R.V.; Teodoro, A.A.M.; Rosa, R.L.; Saadi, M.; Melgarejo, D.C.; Nardelli, P.H.J.; Rodríguez, D.Z. Intrusion detection system based on fast hierarchical deep convolutional neural network. IEEE Access
**2021**, 9, 61024–61034. [Google Scholar] [CrossRef] - Mendonça, R.V.; Silva, J.C.; Rosa, R.L.; Saadi, M.; Rodriguez, D.Z.; Farouk, A. A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithm. Expert Syst.
**2021**, 39, e12917. [Google Scholar] [CrossRef] - Padamvathi, V.; Vardhan, B.V.; Krishna, A. Quantum Cryptography and Quantum Key Distribution Protocols: A Survey; IEEE: Piscataway, NJ, USA, 2016; pp. 556–562. [Google Scholar]
- Basso Basset, F.; Valeri, M.; Roccia, E.; Muredda, V.; Poderini, D.; Neuwirth, J.; Spagnolo, N.; Rota, M.B.; Carvacho, G.; Sciarrino, F.; et al. Quantum key distribution with entangled photons generated on demand by a quantum dot. Sci. Adv.
**2021**, 7, eabe6379. [Google Scholar] [CrossRef] - Langenfeld, S.; Thomas, P.; Morin, O.; Rempe, G. Quantum repeater node demonstrating unconditionally secure key distribution. Phys. Rev. Lett.
**2021**, 126, 230506. [Google Scholar] [CrossRef] - Zhang, H.; Ji, Z.; Wang, H.; Wu, W. Survey on quantum information security. China Commun.
**2019**, 16, 1–36. [Google Scholar] [CrossRef] - Manjunatha, V.; Rao, A.; Khan, A. Complex key generation with secured seed exchange for Vernam cipher in security applications. Mater. Today Proc.
**2021**, 35, 497–500. [Google Scholar] [CrossRef] - Zhao, L.Y.; Wu, Q.J.; Qiu, H.K.; Qian, J.L.; Han, Z.F. Practical security of wavelength-multiplexed decoy-state quantum key distribution. Phys. Rev. A
**2021**, 103, 022429. [Google Scholar] [CrossRef] - Tang, G.Z.; Li, C.Y.; Wang, M. Polarization discriminated time-bin phase-encoding measurement-device-independent quantum key distribution. Quantum Eng.
**2021**, 3, e79. [Google Scholar] [CrossRef] - Li, J.H.; Shi, L.; Li, T.X.; Xue, Y.; Zhang, Z.Y.; Tang, J. Parameters optimization based on neural network of practical wavelength division multiplexed decoy-state quantum key distribution. Mod. Phys. Lett. B
**2021**, 35, 2150479. [Google Scholar] [CrossRef] - Wang, W.; Lo, H.K. Machine learning for optimal parameter prediction in quantum key distribution. Phys. Rev. A
**2019**, 100, 062334. [Google Scholar] [CrossRef] [Green Version] - Yi, Y.; Rao, Y.; Huang, C.; Zeng, S.; Yang, Y.; He, Q.; Chen, X. Optimization of quantum key distribution parameters based on random forest. In Proceedings of the 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), Yibin, China, 20–22 August 2021; pp. 164–168. [Google Scholar] [CrossRef]
- Wang, M.; Zhu, T.; Zhang, T.; Zhang, J.; Yu, S.; Zhou, W. Security and privacy in 6G networks: New areas and new challenges. Digit. Commun. Netw.
**2020**, 6, 281–291. [Google Scholar] [CrossRef] - Abdel Hakeem, S.A.; Hussein, H.H.; Kim, H. Security requirements and challenges of 6G technologies and applications. Sensors
**2022**, 22, 1969. [Google Scholar] [CrossRef] - Rodríguez, D.Z.; Rosa, R.L.; Almeida, F.L.; Mittag, G.; Möller, S. Speech quality assessment in wireless communications with mimo systems using a parametric model. IEEE Access
**2019**, 7, 35719–35730. [Google Scholar] [CrossRef] - Dawy, Z.; Saad, W.; Ghosh, A.; Andrews, J.G.; Yaacoub, E. Toward massive machine type cellular communications. IEEE Wirel. Commun.
**2016**, 24, 120–128. [Google Scholar] [CrossRef] - Zhou, X.Y.; Ding, H.J.; Zhang, C.H.; Li, J.; Zhang, C.M.; Wang, Q. Experimental three-state measurement-device-independent quantum key distribution with uncharacterized sources. Opt. Lett.
**2020**, 45, 4176–4179. [Google Scholar] [CrossRef] - Lakshmi, P.S.; Murali, G. Comparison of classical and quantum cryptography using QKD simulator. In Proceedings of the International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India, 1–2 August 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 3543–3547. [Google Scholar]
- Roy, D.; Panda, P.; Roy, K. Tree-CNN: A hierarchical deep convolutional neural network for incremental learning. Neural Netw.
**2020**, 121, 148–160. [Google Scholar] [CrossRef] - Al-Mohammed, H.A.; Yaacoub, E. On the use of quantum communications for securing IoT devices in the 6G era. In Proceedings of the 2021 IEEE International Conference on Communications Workshops (ICC Workshops), Chicago, IL, USA, 1 January 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 1–6. [Google Scholar]
- Takeoka, M.; Guha, S.; Wilde, M.M. Fundamental rate-loss tradeoff for optical quantum key distribution. Nat. Commun.
**2014**, 5, 5235. [Google Scholar] [CrossRef] [Green Version]

**Figure 1.**Techniques for achieving information security, representing the schemes of: (

**a**) a classic cryptographic; (

**b**) a PLS-based cryptographic; (

**c**) a QKD-based cryptographic.

**Figure 3.**A generic model of 2-level Tree-CNN that is used to build the proposed QKD protocol selector.

**Figure 5.**Analysis of the protocols BB84, MDI, and TF for a transmission distance range of 0 to 250 km, considering different learning algorithms for QKD implementations.

**Figure 6.**Analysis of the protocols BB84, MDI, and TF for a transmission distance range of 250 to 500 km, considering different learning algorithms for QKD implementations.

**Figure 7.**Analysis of the protocols BB84, MDI, and TF for transmission distances larger than 500 km, considering different learning algorithms for QKD implementations.

Tree-CNN | SVM | CNN | KNN | |
---|---|---|---|---|

AUC | 99.89 | 98.17 | 97.02 | 95.76 |

Model | Sensitivity | F1-Measure | G-Mean |
---|---|---|---|

Proposed | 0.9913 | 0.9895 | 0.9906 |

RF [23] | 0.9814 | 0.9726 | 0.9801 |

Tree-CNN | CNN | SVM | B [23] | |
---|---|---|---|---|

Time Cost | 0.65 | 2.3 | 3.1 | 0.65 |

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**MDPI and ACS Style**

Okey, O.D.; Maidin, S.S.; Lopes Rosa, R.; Toor, W.T.; Carrillo Melgarejo, D.; Wuttisittikulkij, L.; Saadi, M.; Zegarra Rodríguez, D.
Quantum Key Distribution Protocol Selector Based on Machine Learning for Next-Generation Networks. *Sustainability* **2022**, *14*, 15901.
https://doi.org/10.3390/su142315901

**AMA Style**

Okey OD, Maidin SS, Lopes Rosa R, Toor WT, Carrillo Melgarejo D, Wuttisittikulkij L, Saadi M, Zegarra Rodríguez D.
Quantum Key Distribution Protocol Selector Based on Machine Learning for Next-Generation Networks. *Sustainability*. 2022; 14(23):15901.
https://doi.org/10.3390/su142315901

**Chicago/Turabian Style**

Okey, Ogobuchi Daniel, Siti Sarah Maidin, Renata Lopes Rosa, Waqas Tariq Toor, Dick Carrillo Melgarejo, Lunchakorn Wuttisittikulkij, Muhammad Saadi, and Demóstenes Zegarra Rodríguez.
2022. "Quantum Key Distribution Protocol Selector Based on Machine Learning for Next-Generation Networks" *Sustainability* 14, no. 23: 15901.
https://doi.org/10.3390/su142315901