Abstract
Using a global optimization algorithm to optimize the initial weights and thresholds of traditional neural network models can effectively address the problems of premature convergence and lower accuracy. However, the shortcomings such as slow convergence speed and poor local search ability still exist. In order to solve these problems, a neural network model QGA–QGCNN using a Quantum Genetic Algorithm (QGA) to optimize Quantum Gate Circuit Neural Network (QGCNN) is proposed in this paper. In QGA–QGCNN, the initial parameters of QGCNN are optimized for the strong global optimization ability and faster convergence speed by using a QGA. When dealing with more complex problems, the QGCNN model based on quantum computing has specific parallel computing capabilities and can give full play to its ability to blur uncertain problems, thereby improving detection performance. We use the authoritative 10% KDD CUP99 data set in the field of network intrusion detection to conduct simulation experiments on the proposed QGA–QGCNN model. Experimental results show that the proposed intrusion detection model has a lower false alarm rate and significant accuracy compared to conventional attack detection models. And QGCNN optimized by QGA improves the convergence performance of the model.
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All data, models and code generated and used during the current study are available from the corresponding author on reasonable request.
References
Cao B, Fan S, Zhao J, Yang P, Muhammad K, Tanveer M (2020) Quantum-enhanced multiobjective large-scale optimization via parallelism. Swarm Evolut Comput 57:100697
Kak SC (1995) On quantum neural computing. Inf Sci 83:143–160
Menneer T, Narayanan A (1995) Quantum-inspired neural networks. Tech Rep R329
Narayanan A, Menneer T (2000) Quantum artificial neural network architectures and components. Inf Sci 128(3):231–255
Behrman EC, Nash LR, Steck JE, Chandrashekar VG, Skinner SR (2000) Simulations of quantum neural networks. Inf Sci 128(3):257–269
Zhu D, Chen EK, Yang Y (2005) A quantum neural networks data fusion algorithm and its application for fault diagnosis. Springer, Berlin
Svitek M (2008) Wave probabilisties and quantum entanglement. Neural Netw World 18(5):401–406
Li P, Xiao H (2014) Model and algorithm of quantum-inspired neural network with sequence input based on controlled rotation gates. Appl Intell 40(1):107–126
Ganjefar S, Tofighi M (2018) Optimization of quantum-inspired neural network using memetic algorithm for function approximation and chaotic time series prediction. Neurocomputing 291:175–186
Cong I, Choi S, Lukin MD (2019) Quantum convolutional neural networks. Nat Phys 15(12):1273–1278
Schuld M, Bocharov A, Svore KM, Wiebe N (2020) Circuit-centric quantum classifiers. Phys Rev A 101:032308
Shi J, Tang Y, Lu Y, Feng Y, Shi R, Zhang S (2023) Quantum circuit learning with parameterized boson sampling. IEEE Trans Knowl Data Eng 35:1965–1976
Shi J, Wang W, Lou X, Zhang S, Li X (2021) Parameterized Hamiltonian learning with quantum circuit. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TPAMI.2022.3203157
Dunjko V, Briegel HJ (2018) Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Rep Prog Phys 81(7):074001
Abbas A, Sutter D, Zoufal C, Lucchi A, Figalli A, Woerner S (2021) The power of quantum neural networks. Nat Comput Sci 1(6):403–409
Shi J, Li Z, Lai W, Li F, Shi R, Feng Y, Zhang S (2021) Two end-to-end quantum-inspired deep neural networks for text classification. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2021.3130598
Ali MM, Toern A (2004) Population set-based global optimization algorithms: some modifications and numerical studies. Comput Oper Res 31(10):1703–1725
Chakraborty S, Saha AK, Sharma S, Mirjalili S, Chakraborty R (2020) A novel enhanced whale optimization algorithm for global optimization. Comput Ind Eng 153(5):107086
Zhang J (2019) Derivative-free global optimization algorithms: population based methods and random search approaches. arXiv preprint arXiv:1904.09368
Yue X, Zhang H, Yu H (2020) A hybrid grasshopper optimization algorithm with invasive weed for global optimization. IEEE Access 8:1–1
Han K-H, Kim J-H (2000) Genetic quantum algorithm and its application to combinatorial optimization problem, vol. 2, pp. 1354–13602
PanChi L, Song KaoPing YE (2011) Controlled-rotating-gate-based quantum neural networks model and its algorithm with application. Control Decis 26(6):898–901906
Chao Z, Ping P, Liang H (2018) Risk assessment of information security based on quantum gate circuit neural networks. Comput Eng 44(12):39–45
LeCun YH, Bengio Y, Geoffrey H (2015) Deep learning. Nature 521(7553):436–444
Bottou L, Bousquet O (2007) The tradeoffs of large scale learning. Adv Neural Inf Process System 20:161–168
Selvi RS, Valarmathi ML (2020) Optimal feature selection for big data classification: firefly with lion-assisted model. Big Data 8(2):125–146
HongPing Z, QingGe G, ZhanBo L (2012) Feature selection of intrusion detection based on genetic algorithm. Appl Res Comput 29(4):1417–14191426
Ashfaq RAR, Wang X-Z, Huang JZ, Abbas H, He Y-L (2017) Fuzziness based semi-supervised learning approach for intrusion detection system. Inf Sci 378:484–497
Koc L, Mazzuchi TA, Sarkani S (2012) A network intrusion detection system based on a hidden naïve bayes multiclass classifier. Exp Syst Appl 39(18):13492–13500
Song HM, Woo J, Kim HK (2020) In-vehicle network intrusion detection using deep convolutional neural network. Veh Commun 21:100198
Information, Computer Science University of California, I. KDD Cup 1999: Computer network intrusion detection (2013)
Mohseni SA, Tan AH (2012) Optimization of neural networks using variable structure systems. IEEE Trans Syst Man Cybern. Part B, Cybern: Publ IEEE Syst Man Cybernet Soc 42(6):1645
Dudek G, Pelka P, Smyl S (2021) A hybrid residual dilated lstm and exponential smoothing model for midterm electric load forecasting. IEEE Trans Neural Netw Learn Syst 33(7):2879–2891
Purushothaman G, Karayiannis NB (1997) Quantum neural networks (qnns): inherently fuzzy feedforward neural networks. IEEE Trans Neural Netw 8(3):679–693
Funding
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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Gong, C., Zhu, H., Gani, A. et al. QGA–QGCNN: a model of quantum gate circuit neural network optimized by quantum genetic algorithm. J Supercomput 79, 13421–13441 (2023). https://doi.org/10.1007/s11227-023-05158-7
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DOI: https://doi.org/10.1007/s11227-023-05158-7