由于内部人员具有访问组织内部资源的权限,其行为出现漏洞或故意威胁所产生的影响对组织而言可能是巨大的损失.因此,内部人员威胁行为研究对保障系统安全具有重要价值.针对大量内部威胁行为的具体识别,本文提出了一种两步用户威胁行为...由于内部人员具有访问组织内部资源的权限,其行为出现漏洞或故意威胁所产生的影响对组织而言可能是巨大的损失.因此,内部人员威胁行为研究对保障系统安全具有重要价值.针对大量内部威胁行为的具体识别,本文提出了一种两步用户威胁行为活动分析识别策略.首先,利用粒子群优化(Particle Swarm Optimization,PSO)算法优化具有噪声的基于密度的聚类方法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)来实现对偏离正常模式的离群点的精准查找.第2步,建立多特征权重联合评估策略,基于上一步的离群点分析结果,采用多指标评价方法实现对用户威胁行为的识别.实验结果表明,所提PSO-DBSCAN算法能更好地缩减初步离群点,并在特征约简后行为活动识别准确率最高达到99.58%,对威胁活动的识别具有有效性.展开更多
Accurate meteorological predictions in the Arctic are important in response to the rapid climate change and insufficient meteorological observations in the Arctic.In this study,we adopted a high-resolution Weather Res...Accurate meteorological predictions in the Arctic are important in response to the rapid climate change and insufficient meteorological observations in the Arctic.In this study,we adopted a high-resolution Weather Research and Forecasting(WRF)model to simulate the meteorology at two Arctic stations(Barrow and Summit)in April 2019.Simulation results were also evaluated by using surface measurements and statistical parameters.In addition,weather charts during the studied time period were also used to assess the model performance.The results demonstrate that the WRF model is able to accurately capture the meteorological parameters for the two Arctic stations and the weather systems such as cyclones and anticyclones in the Arctic.Moreover,we found the model performance in predicting the surface pressure the best while the performance in predicting the wind the worst among these meteorological predictions.However,the wind predictions at these Arctic stations were found to be more accurate than those at urban stations in mid-latitude regions,due to the differences in land features and anthropogentic heat sources between these regions.In addition,a comparison of the simulation results showed that the prediction of meteorological conditions at Summit is superior to that at Barrow.Possible reasons for the deviations in temperature predictions between these two Arctic stations are uncertainties in the treatments of the sea ice and the cloud in the model.With respect to the wind,the deviations may source from the overestimation of the wind over the sea and at coastal stations.展开更多
文摘由于内部人员具有访问组织内部资源的权限,其行为出现漏洞或故意威胁所产生的影响对组织而言可能是巨大的损失.因此,内部人员威胁行为研究对保障系统安全具有重要价值.针对大量内部威胁行为的具体识别,本文提出了一种两步用户威胁行为活动分析识别策略.首先,利用粒子群优化(Particle Swarm Optimization,PSO)算法优化具有噪声的基于密度的聚类方法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)来实现对偏离正常模式的离群点的精准查找.第2步,建立多特征权重联合评估策略,基于上一步的离群点分析结果,采用多指标评价方法实现对用户威胁行为的识别.实验结果表明,所提PSO-DBSCAN算法能更好地缩减初步离群点,并在特征约简后行为活动识别准确率最高达到99.58%,对威胁活动的识别具有有效性.
基金funded by the National Key Research and Development Program of China(Grant no.2022YFC3701204)the 2023 Outstanding Young Backbone Teacher of Jiangsu“Qinglan”Project(Grant no.R2023Q02)the National Natural Science Foundation of China(Grant no.41705103).
文摘Accurate meteorological predictions in the Arctic are important in response to the rapid climate change and insufficient meteorological observations in the Arctic.In this study,we adopted a high-resolution Weather Research and Forecasting(WRF)model to simulate the meteorology at two Arctic stations(Barrow and Summit)in April 2019.Simulation results were also evaluated by using surface measurements and statistical parameters.In addition,weather charts during the studied time period were also used to assess the model performance.The results demonstrate that the WRF model is able to accurately capture the meteorological parameters for the two Arctic stations and the weather systems such as cyclones and anticyclones in the Arctic.Moreover,we found the model performance in predicting the surface pressure the best while the performance in predicting the wind the worst among these meteorological predictions.However,the wind predictions at these Arctic stations were found to be more accurate than those at urban stations in mid-latitude regions,due to the differences in land features and anthropogentic heat sources between these regions.In addition,a comparison of the simulation results showed that the prediction of meteorological conditions at Summit is superior to that at Barrow.Possible reasons for the deviations in temperature predictions between these two Arctic stations are uncertainties in the treatments of the sea ice and the cloud in the model.With respect to the wind,the deviations may source from the overestimation of the wind over the sea and at coastal stations.
