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<p>To address network security challenges in complex networks, a quantum neural network-based network security situational awareness algorithm was studied. This approach combined a VQNN network with a QCNN network and optimized its parameters using a parallel particle swarm optimization algorithm to address the slow convergence of the model in complex network scenarios. This model was compared with other classical algorithms on the KDDCUP99 and UNSW-NB15 datasets. Experimental results demonstrated that the proposed model exhibited significant advantages across nearly all evaluation metrics. Compared to the comparison models, the proposed model performed significantly better in terms of network security situational awareness across three evaluation metrics. Taking the comparison of MSE index as an example, the MSE value of this research model is 0.0078, which is lower than 0.0091 of PSO-VQNN model and 0.1041 of classical CNN model, which proves its effectiveness.</p>

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Research on network security situation awareness algorithm based on quantum neural network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月7日

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  • Nan Li,
  • Yu Wang,
  • Haibo Zhang,
  • Zhiqiang Li,
  • Weina Zhao

摘要

To address network security challenges in complex networks, a quantum neural network-based network security situational awareness algorithm was studied. This approach combined a VQNN network with a QCNN network and optimized its parameters using a parallel particle swarm optimization algorithm to address the slow convergence of the model in complex network scenarios. This model was compared with other classical algorithms on the KDDCUP99 and UNSW-NB15 datasets. Experimental results demonstrated that the proposed model exhibited significant advantages across nearly all evaluation metrics. Compared to the comparison models, the proposed model performed significantly better in terms of network security situational awareness across three evaluation metrics. Taking the comparison of MSE index as an example, the MSE value of this research model is 0.0078, which is lower than 0.0091 of PSO-VQNN model and 0.1041 of classical CNN model, which proves its effectiveness.