摘要
当前网络安全形势日益严峻,为了提高网络安全态势预测的准确性,提出了一种基于人群搜索算法优化BP神经网络的网络安全态势预测方法.本算法利用人群搜索算法特有的利己、利他、预动和不确定推理四大行为特征确定搜索策略,找到最佳适应度个体,获取最优权值和阈值,然后再对BP神经网络的随机初始阈值和权值进行赋值,经过神经网络训练后得到预测值,最后与其它两种优化算法得到的预测值进行对比.实验表明,该算法用于网络安全态势预测精准度更高,误差更小,并具有更好的稳定性.
The current cybersecurity situation is getting worse.In order to improve the accuracy of network security situation prediction,a network security situation prediction method based on SOA_BP neural network is proposed.The algorithm uses the four behavioral characteristics of SOA algorithm:the self-interest,altruism,pre-action and uncertainty reasoning to determine the search strategy,find the best fitness individual,obtain the optimal weight and threshold,and then assign them to the random initial weights and thresholds.After training the neural network,the predicted values are obtained.Finally,it is compared with the predicted values obtained by othertwo optimization algorithms.The experimental results show that this prediction algorithm has higher accuracy,smaller error and better stability.
作者
张然
刘敏
张启坤
甘勇
ZHANG Ran;LIU Min;ZHANG Qi-kun;GAN Yong(School of Computer and Communication Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002,China;School of Information Engineering,Zhengzhou Institute of Technology,Zhengzhou 450044,China)
出处
《微电子学与计算机》
北大核心
2020年第6期62-65,69,共5页
Microelectronics & Computer
基金
国家自然科学基金项目(61772477)
河南省重点科技攻关项目(142102210081)
河南省高等学校重点科研项目计划支持(13B520322)
河南省产学研合作项目(132107000066)。
关键词
BP神经网络
人群搜索算法
网络安全
态势预测
BP neural network
seeker optimization algorithm
network security
situation prediction
