由于网络环境攻击手段的多样性,导致误报率较高,设计一种基于改进随机森林算法的风电场通信网络攻击预警方法。融合卷积神经网络与随机森林算法提取风电场通信网络攻击特征。引入攻击频次指标和滑动窗口来动态评估实际攻击次数占比,并...由于网络环境攻击手段的多样性,导致误报率较高,设计一种基于改进随机森林算法的风电场通信网络攻击预警方法。融合卷积神经网络与随机森林算法提取风电场通信网络攻击特征。引入攻击频次指标和滑动窗口来动态评估实际攻击次数占比,并量化攻击频率指数(Attack Frequency Index,AFI)作为预警阈值,结合所构建的预警指标体系与预警等级,实现风电场通信网络攻击预警。实验结果表明,设计方法的平均误报率仅为7.93%,平均响应时间为29.67 ms,且波动较小,显示出更高的稳定性和可靠性。展开更多
An improved artificial bee colony-random forest(IABC-RF)model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit.A new search strategy of the artificial bee colony(AB...An improved artificial bee colony-random forest(IABC-RF)model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit.A new search strategy of the artificial bee colony(ABC)algorithm is herein developed and incorporated,with the results showing that a much higher computational efficiency can be achieved with the new model,while high computational accuracy can also be maintained.The improved ABC algorithm is thereafter utilised and combined with the random forest(RF)model,where four important hyper-parameters are optimized,for a tunnel deformation prediction.Results are thoroughly compared with those of other prediction methods based on machine learning(ML),as well as the monitored data on the site.Via the comparisons,the validity and effectiveness of the proposed model are fully demonstrated,and a more promising perspective can be seen of the method for its potential wide applications in geotechnical engineering.展开更多
文摘由于网络环境攻击手段的多样性,导致误报率较高,设计一种基于改进随机森林算法的风电场通信网络攻击预警方法。融合卷积神经网络与随机森林算法提取风电场通信网络攻击特征。引入攻击频次指标和滑动窗口来动态评估实际攻击次数占比,并量化攻击频率指数(Attack Frequency Index,AFI)作为预警阈值,结合所构建的预警指标体系与预警等级,实现风电场通信网络攻击预警。实验结果表明,设计方法的平均误报率仅为7.93%,平均响应时间为29.67 ms,且波动较小,显示出更高的稳定性和可靠性。
基金sponsored by the National Natural Science Foundation of China(Grant Nos.52178386,51808193,and 51979270).
文摘An improved artificial bee colony-random forest(IABC-RF)model is proposed for predicting the tunnel deformation due to the excavation of an adjacent foundation pit.A new search strategy of the artificial bee colony(ABC)algorithm is herein developed and incorporated,with the results showing that a much higher computational efficiency can be achieved with the new model,while high computational accuracy can also be maintained.The improved ABC algorithm is thereafter utilised and combined with the random forest(RF)model,where four important hyper-parameters are optimized,for a tunnel deformation prediction.Results are thoroughly compared with those of other prediction methods based on machine learning(ML),as well as the monitored data on the site.Via the comparisons,the validity and effectiveness of the proposed model are fully demonstrated,and a more promising perspective can be seen of the method for its potential wide applications in geotechnical engineering.
