随着网络技术的快速发展,网络攻击带来了极大的负面影响,因此网络安全问题亟待解决。针对网络攻击中的拒绝服务(Denial of Service,DoS)攻击,提出了一种基于边缘计算框架的孤立森林网络异常检测方法。该方法根据每个边缘节点的特性实现...随着网络技术的快速发展,网络攻击带来了极大的负面影响,因此网络安全问题亟待解决。针对网络攻击中的拒绝服务(Denial of Service,DoS)攻击,提出了一种基于边缘计算框架的孤立森林网络异常检测方法。该方法根据每个边缘节点的特性实现对模型训练任务的合理分配,有效地提高了边缘节点的利用效率;同时,利用边缘计算的特点实现了对云中心模型训练任务的分流,从而更好地减少系统的耗时,减轻云中心的任务负担。为了验证所提方法的有效性,对10%-KDDCUP99网络数据集进行预处理,并提取部分数据用于实验。实验结果表明,与支持向量机(Support Vector Machine,SVM)和多层感知器(Multi-Layer Perceptron,MLP)方法相比,所提方法将系统建立时间分别缩短了90%和60%,且得出的曲线下面积(Area Under Curve,AUC)可达0.9以上,这证明该方法能够在确保较高异常检测性能条的件下有效减少异常检测系统的建立时间。展开更多
有载分接开关(on-load tap changer,OLTC)是电力变压器中唯一可以运动的机构,具有复杂的机械结构。为了解决有载分接开关机械故障诊断中样本库无法涵盖所有故障类型的问题,文中提出了一种基于驱动电机电流与振动信号的有载分接开关故障...有载分接开关(on-load tap changer,OLTC)是电力变压器中唯一可以运动的机构,具有复杂的机械结构。为了解决有载分接开关机械故障诊断中样本库无法涵盖所有故障类型的问题,文中提出了一种基于驱动电机电流与振动信号的有载分接开关故障诊断方法。首先,文中研究了有载分接开关多源监测信号的特征提取方法,提出了驱动电机电流信号的持续时间、绝对值之和以及开关本体振动信号的经验模态分解能量等特征。接着,文中结合随机森林(random forest)和孤立森林(isolation forest)算法,提出了一种与异常检测相融合的有载分接开关故障诊断方法。最后,文中对VRG型有载分接开关进行了实验,采集了正常、护罩松动、连杆窜动、触头松动4类样本数据,实验结果表明,文中所提出的方法对已知故障的诊断准确率达97.78%,对未知故障的识别准确率达92.5%。展开更多
Constructing a statistical model that best fits the background is a key step in geochemical anomaly identification. But the model is hard to be constructed in situations where the sample population has unknown and/or ...Constructing a statistical model that best fits the background is a key step in geochemical anomaly identification. But the model is hard to be constructed in situations where the sample population has unknown and/or complex distribution. Isolation forest is an outlier detection approach that explicitly isolates anomaly samples rather than models the population distribution. It can extract multivariate anomalies from huge-sized high-dimensional data with unknown population distribution. For this reason,we tentatively applied the method to identify multivariate anomalies from the stream sediment survey data of the Lalingzaohuo district,an area with a complex geological setting,in Qinghai Province in China. The performance of the isolation forest algorithm in anomaly identification was compared with that of a continuous restricted Boltzmann machine. The results show that the isolation forest model performs superiorly to the continuous restricted Boltzmann machine in multivariate anomaly identification in terms of receiver operating characteristic curve,area under the curve,and data-processing efficiency. The anomalies identified by the isolation forest model occupy 19% of the study area and contain 82% of the known mineral deposits,whereas the anomalies identified by the continuous restricted Boltzmann machine occupy 35% of the study area and contain 88% of the known mineral deposits. It takes 4. 07 and 279. 36 seconds respectively handling the dataset using the two models. Therefore,isolation forest is a useful anomaly detection method that can quickly extract multivariate anomalies from geochemical exploration data.展开更多
文摘随着网络技术的快速发展,网络攻击带来了极大的负面影响,因此网络安全问题亟待解决。针对网络攻击中的拒绝服务(Denial of Service,DoS)攻击,提出了一种基于边缘计算框架的孤立森林网络异常检测方法。该方法根据每个边缘节点的特性实现对模型训练任务的合理分配,有效地提高了边缘节点的利用效率;同时,利用边缘计算的特点实现了对云中心模型训练任务的分流,从而更好地减少系统的耗时,减轻云中心的任务负担。为了验证所提方法的有效性,对10%-KDDCUP99网络数据集进行预处理,并提取部分数据用于实验。实验结果表明,与支持向量机(Support Vector Machine,SVM)和多层感知器(Multi-Layer Perceptron,MLP)方法相比,所提方法将系统建立时间分别缩短了90%和60%,且得出的曲线下面积(Area Under Curve,AUC)可达0.9以上,这证明该方法能够在确保较高异常检测性能条的件下有效减少异常检测系统的建立时间。
文摘有载分接开关(on-load tap changer,OLTC)是电力变压器中唯一可以运动的机构,具有复杂的机械结构。为了解决有载分接开关机械故障诊断中样本库无法涵盖所有故障类型的问题,文中提出了一种基于驱动电机电流与振动信号的有载分接开关故障诊断方法。首先,文中研究了有载分接开关多源监测信号的特征提取方法,提出了驱动电机电流信号的持续时间、绝对值之和以及开关本体振动信号的经验模态分解能量等特征。接着,文中结合随机森林(random forest)和孤立森林(isolation forest)算法,提出了一种与异常检测相融合的有载分接开关故障诊断方法。最后,文中对VRG型有载分接开关进行了实验,采集了正常、护罩松动、连杆窜动、触头松动4类样本数据,实验结果表明,文中所提出的方法对已知故障的诊断准确率达97.78%,对未知故障的识别准确率达92.5%。
基金Supported by projects of the National Natural Science Foundation of China(Nos.41272360,41472299,41672322)
文摘Constructing a statistical model that best fits the background is a key step in geochemical anomaly identification. But the model is hard to be constructed in situations where the sample population has unknown and/or complex distribution. Isolation forest is an outlier detection approach that explicitly isolates anomaly samples rather than models the population distribution. It can extract multivariate anomalies from huge-sized high-dimensional data with unknown population distribution. For this reason,we tentatively applied the method to identify multivariate anomalies from the stream sediment survey data of the Lalingzaohuo district,an area with a complex geological setting,in Qinghai Province in China. The performance of the isolation forest algorithm in anomaly identification was compared with that of a continuous restricted Boltzmann machine. The results show that the isolation forest model performs superiorly to the continuous restricted Boltzmann machine in multivariate anomaly identification in terms of receiver operating characteristic curve,area under the curve,and data-processing efficiency. The anomalies identified by the isolation forest model occupy 19% of the study area and contain 82% of the known mineral deposits,whereas the anomalies identified by the continuous restricted Boltzmann machine occupy 35% of the study area and contain 88% of the known mineral deposits. It takes 4. 07 and 279. 36 seconds respectively handling the dataset using the two models. Therefore,isolation forest is a useful anomaly detection method that can quickly extract multivariate anomalies from geochemical exploration data.
