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Network attack detection scheme based on variational quantum neural network

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Abstract

Network attack may have a serious impact on network security. With the rapid development of quantum machine learning, variational quantum neural network (VQNN) has demonstrated quantum advantages in classification problems. The intrusion detection system (IDS) based on quantum machine learning has higher accuracy and efficiency than the IDS based on traditional machine learning. In this work, we propose a intrusion detection scheme based on VQNN, which is composed of variational quantum circuit (VQC) and classical machine learning (ML) strategy. In order to verify the effectiveness of the scheme, we used the VQNN model and some classic ML models (Such as artificial neural network, support vector machines, K-Nearest Neighbors, Naive Bayes, decision tree) to conduct comparative experiments. The results indicate that the proposed IDS model based on VQNN has a 97.21% precision, which is higher than other classic IDS models. Furthermore, our VQC can be deployed on the overwhelming majority of recent noisy intermediate-scale quantum machines (such as IBM). This research will contribute to the construction of general variational quantum framework and experimental design and highlight the potential hopes and challenges of hybrid quantum classical learning schemes.

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References

  1. Ukita J (2020) Causal importance of low-level feature selectivity for generalization in image recognition. Neural Netw 125:185–193

    Article  Google Scholar 

  2. Gong C, Du J (2020) Grover algorithm-based quantum homomorphic encryption ciphertext retrieval scheme in quantum cloud computing. Quantum Inf Process 19(3):105

    Article  Google Scholar 

  3. Gong C, Shi T (2020) An improved quantum genetic algorithms and application for ddos attack detection. In: 2019 IEEE International Conference on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

  4. AruteF BR, Arya K (2019) Quantum supremacy using a programmable superconducting processor. Nature 574(7779):505–510

    Article  Google Scholar 

  5. Kak SC (1995) Quantum neural computing. Syst Control Inf 52(3–4):143–160

    Google Scholar 

  6. Li P, Li S (2008) Learning algorithm and application of quantum bp neural networks based on universal quantum gates. J Syst Eng Electron 19(1):167–174

    Article  Google Scholar 

  7. Patrick R, Thomas B, Christian W, Seth L (2018) Quantum hopfield neural network. Phys Rev A 98(4):042308

    Article  Google Scholar 

  8. Leonardo B, Nicola P, Sougato B (2016) Quantum gate learning in qubit networks: toffoli gate without time-dependent control. npj Quantum Inf 2:16019

  9. P J (2018) Quantum computing in the nisq era and beyond. Quantum 2:79

    Article  Google Scholar 

  10. Jiang Z, Zhihui W, Rieffel EG (2017) Near-optimal quantum circuit for Grover's unstructured search using a transverse field. Phys Rev A 95(6):62317

    Article  Google Scholar 

  11. Huang C, Newman M SM (2020) Explicit lower bounds on strong quantum simulation. IEEE Trans Inf Theory 69:5585–5600

    Article  MathSciNet  Google Scholar 

  12. Cintas Canto A, Kermani MM, Azarderakhsh R (2021) Reliable architectures for composite-field-oriented constructions of McEliece post-quantum cryptography on FPGA. IEEE Trans Comput Aided Des Integr Circuits Syst 40:999–1003

    Article  Google Scholar 

  13. Wei AY, Wea HA, Naik P (2020) Quantum algorithms for jet clustering. Phys Rev D 101:94015

    Article  MathSciNet  Google Scholar 

  14. Qiu P-H, SY M, Chen XG (2019) Detecting entanglement with deep quantum neural networks. IEEE Access 7:94310–94320

    Article  Google Scholar 

  15. Xia RKS (2020) Hybrid quantum-classical neural network for calculating ground state energies of molecules. Entropy 22:828

    Article  MathSciNet  Google Scholar 

  16. VerteletskyiV IAF, Yen TC (2020) Measurement optimization in the variational quantum eigensolver using a minimum clique cover. J Chem Phys 152:124114

    Article  Google Scholar 

  17. PepperA PGJ, Tischler N (2019) Experimental realization of a quantum autoencoder: the compression of qutrits via machine learning. Phys Rev Lett 122:60501

    Article  Google Scholar 

  18. Zhou L, Sea C, Wang ST (2020) Quantum approximate optimization algorithm: performance, mechanism, and implementation on near-term devices. Phys Rev X 10:21067

    Google Scholar 

  19. Cong I, Choi LMDS (2019) Quantum convolutional neural networks. Nat Phys 15:1273–1278

    Article  Google Scholar 

  20. Dubosq C, Calvo RMEAF (2020) Quantum modeling of the optical spectra of carbon cluster structural families and relation to the interstellar extinction uv bump. Astromy Astrophyscs 634:13

    Google Scholar 

  21. YanyingLiang WP, Zhu-JunZheng OS, Zhao G (2021) A hybrid quantum-classical neural network with deep residual learning. Elsevier, New York

    Google Scholar 

  22. SteveAbel JCC, Spannowsky M (2022) Completely quantum neural networks. CoRR

  23. Yevhenii T, SergiiStirenko OROAEP, Gordienko Y (2021) Hybrid classic-quantum neural networks for image classification. In: The 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications

  24. JunQi CHHY, Chen P (2021) Qtn-vqc: an end-to-end learning framework for quantum neural networks. CoRR

  25. Jiang W, Xiong J, Shi Y (2020) Can quantum computers learn like classical computers? a co-design framework for machine learning and quantum circuits. arXiv:2006.14815

  26. Li P, Wang B (2020) Quantum neural networks model based on swap test and phase estimation. Neural Netw 130:152–164

    Article  Google Scholar 

  27. Kerstin B, Dmytro B, Terry F, J OT, Robert S, Ramona W (2019) Efficient learning for deep quantum neural networks. arXiv:1902.10445

  28. Havlíček V, Córcoles AD, Temme K, Harrow AW, Kandala A, Chow JM, Gambetta JM (2019) Supervised learning with quantum-enhanced feature spaces. Nature 567(7747):209–212

    Article  Google Scholar 

  29. McClean JR, Boixo S, Smelyanskiy VN, Babbush R, Neven H (2018) Barren plateaus in quantum neural network training landscapes. Nat Commun 9(1):1–6

    Article  Google Scholar 

  30. Qiu P-H, Chen X-G, Shi Y-W (2019) Detecting entanglement with deep quantum neural networks. IEEE Access 7:94310–94320

    Article  Google Scholar 

  31. Kdd cup 1999 dataset. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

  32. Khalvati L, Keshtgary M, Rikhtegar N (2018) Intrusion detection based on a novel hybrid learning approach. Nat Commun 6(1):157–162

    Google Scholar 

  33. Liu J, Zhang W, Tang Z, Xie Y, Ma T, Zhang J, Zhang G, Niyoyita JP (2020) Adaptive intrusion detection via ga-gogmm-based pattern learning with fuzzy rough set-based attribute selection. Expert Syst Appl 139:112845

    Article  Google Scholar 

  34. Aljawarneh S, Aldwairi M, Yassein MB (2018) Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J Comput Sci 25:152–160

    Article  Google Scholar 

  35. Mohammadi S, Mirvaziri H, Ghazizadeh-Ahsaee M, Karimipour H (2019) Cyber intrusion detection by combined feature selection algorithm. J Inf Secur Appl 44:80–88

    Google Scholar 

  36. Alazzam H, Sharieh A, Sabri KE (2020) A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert Syst Appl 148:113249

    Article  Google Scholar 

  37. Sim S, Johnson PD, Aspuru-Guzik A (2019) Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Adv Quantum Technol 2:1900070

    Article  Google Scholar 

  38. Yen-Chi Chen S, Huck Yang CH, Qi J, Chen PY, Ma X, Goan HS (2020) Variational quantum circuits for deep reinforcement learning. IEEE Access 8:141007–141024

    Article  Google Scholar 

Download references

Acknowledgements

This work was funded in part by the Liaoning Provincial Department of Education Research under Grant LJKZ0208, in part by the Scientific Research Foundation for Advanced Talents from Shenyang Aerospace University under Grant 18YB06, and National Basic Research Program of China Under Grant JCKY2018410C004.

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Authors and Affiliations

  1. School of Computer Science, Shenyang Aerospace University, Shenyang, 110000, People’s Republic of China

    Changqing Gong, Weiqi Guan & Han Qi

  2. Faculty of Computing and Informatics, University Malaysia Sabah, Labuan International Campus, 87000, Labuan, Malaysia

    Abdullah Gani

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  1. Changqing Gong
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  2. Weiqi Guan
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  3. Abdullah Gani
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  4. Han Qi
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Correspondence to Han Qi.

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Gong, C., Guan, W., Gani, A. et al. Network attack detection scheme based on variational quantum neural network. J Supercomput 78, 16876–16897 (2022). https://doi.org/10.1007/s11227-022-04542-z

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  • DOI: https://doi.org/10.1007/s11227-022-04542-z

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