通过对电力远动监测系统和数据挖掘技术的讨论,提出一种基于马氏距离的双层聚类异常检测算法。针对远动系统数据非球面分布的特点,该算法通过K-means聚类改进算法对数据进行初始分类,然后使用基于马氏距离的Clustering Using Representa...通过对电力远动监测系统和数据挖掘技术的讨论,提出一种基于马氏距离的双层聚类异常检测算法。针对远动系统数据非球面分布的特点,该算法通过K-means聚类改进算法对数据进行初始分类,然后使用基于马氏距离的Clustering Using Representatives(CURE)聚类改进算法对初始分类结果进行优化,以较少的计算成本去除K值设定的影响,达到预期的检测结果。同时,基于马氏距离的CURE聚类改进算法对球面和非球面分布的数据有非常好的适应能力。展开更多
在移动网络环境下,因各移动蜜罐资源有限、攻击注入手段灵活多变,需要动态部署蜜网以协同地检测攻击行为特征。然而现有蜜网易遭受特征识别攻击、网内恶意流量肆意传播、不能跨蜜罐迁移连接。为此,基于软件定义网络(software defined ne...在移动网络环境下,因各移动蜜罐资源有限、攻击注入手段灵活多变,需要动态部署蜜网以协同地检测攻击行为特征。然而现有蜜网易遭受特征识别攻击、网内恶意流量肆意传播、不能跨蜜罐迁移连接。为此,基于软件定义网络(software defined networking,SDN)技术,设计了一种智能协同蜜网(intelligent and collaborative Honeynet,ic-Honeynet)系统。它由逆向连接代理模块和蜜网控制器组成,它的优势在于逐一克服了上述3个缺陷。最后,搭建了一个ic-Honeynet实验环境,并验证了该系统的有效性。实验结果表明:该系统吞吐量近乎线速,高达8.23 Gbit/s;响应时延额外增加很小,仅在0.5～1.2 ms区间变化;连接处理能力也很强,可高达1 473个连接/s。展开更多
The rapid growth of distributed photovoltaic(PV)has remarkable influence for the safe and economic operation of power systems.In view of the wide geographical distribution and a large number of distributed PV power st...The rapid growth of distributed photovoltaic(PV)has remarkable influence for the safe and economic operation of power systems.In view of the wide geographical distribution and a large number of distributed PV power stations,the current situation is that it is dificult to access the current dispatch data network.According to the temporal and spatial characteristics of distributed PV,a graph convolution algorithm based on adaptive learning of adjacency matrix is proposed to estimate the real-time output of distributed PV in regional power grid.The actual case study shows that the adaptive graph convolution model gives different adjacency matrixes for different PV stations,which makes the corresponding output estimation algorithm have higher accuracy.展开更多
文摘通过对电力远动监测系统和数据挖掘技术的讨论,提出一种基于马氏距离的双层聚类异常检测算法。针对远动系统数据非球面分布的特点,该算法通过K-means聚类改进算法对数据进行初始分类,然后使用基于马氏距离的Clustering Using Representatives(CURE)聚类改进算法对初始分类结果进行优化,以较少的计算成本去除K值设定的影响,达到预期的检测结果。同时,基于马氏距离的CURE聚类改进算法对球面和非球面分布的数据有非常好的适应能力。
文摘在移动网络环境下,因各移动蜜罐资源有限、攻击注入手段灵活多变,需要动态部署蜜网以协同地检测攻击行为特征。然而现有蜜网易遭受特征识别攻击、网内恶意流量肆意传播、不能跨蜜罐迁移连接。为此,基于软件定义网络(software defined networking,SDN)技术,设计了一种智能协同蜜网(intelligent and collaborative Honeynet,ic-Honeynet)系统。它由逆向连接代理模块和蜜网控制器组成,它的优势在于逐一克服了上述3个缺陷。最后,搭建了一个ic-Honeynet实验环境,并验证了该系统的有效性。实验结果表明:该系统吞吐量近乎线速,高达8.23 Gbit/s;响应时延额外增加很小,仅在0.5～1.2 ms区间变化;连接处理能力也很强,可高达1 473个连接/s。
基金the Science and Technology Program of State Grid Corporation of China(No.5211TZ1900S6)。
文摘The rapid growth of distributed photovoltaic(PV)has remarkable influence for the safe and economic operation of power systems.In view of the wide geographical distribution and a large number of distributed PV power stations,the current situation is that it is dificult to access the current dispatch data network.According to the temporal and spatial characteristics of distributed PV,a graph convolution algorithm based on adaptive learning of adjacency matrix is proposed to estimate the real-time output of distributed PV in regional power grid.The actual case study shows that the adaptive graph convolution model gives different adjacency matrixes for different PV stations,which makes the corresponding output estimation algorithm have higher accuracy.
