首页|基于改进神经网络的信息安全风险评估模型与指标体系构建研究

基于改进神经网络的信息安全风险评估模型与指标体系构建研究

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研究对传统的神经网络进行了改进,建立了信息安全风险评估模型.在结合现有融合神经网络后,构建了风险评估预测模型.实验结果显示,种群规模大于60且最大迭代次数超过30时,算法准确性不再显著提升,相较传统方法更高效.仿真实验表明,改进算法的预测误差小于1%,验证了其有效性.改进算法的精确度超过90%,明显优于其他算法,证明其适用性和稳定性.
Research on the Construction of Information Security Risk Assessment Model and Index System Based on Improved Neural Network
The traditional neural network is improved,and then the information security risk as-sessment model is established.Combined with the existing fusion neural network,the risk assessment prediction model is constructed.The experimental results show that when the population size is greater than 60 and the maximum number of iterations is more than 30,the accuracy of the algorithm is no lon-ger significantly improved,and it is more efficient than the traditional method.Simulation results show that the prediction error of the improved algorithm is less than 1%,which verifies its effectiveness.The accuracy of the improved algorithm is more than 90%,which is obviously better than other algorithms,proving its applicability and stability.

information securityindicator systemCuckoo search algorithmneural network

高语、单芳芳

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中原工学院计算机学院,河南郑州 475400

信息安全 指标体系 布谷鸟算法 神经网络

2024

佳木斯大学学报(自然科学版)
佳木斯大学

佳木斯大学学报(自然科学版)

影响因子:0.159
ISSN:1008-1402
年,卷(期):2024.42(2)
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