Considering that growing hierarchical self-organizing map(GHSOM) ignores the influence of individual component in sample vector analysis, and its accurate rate in detecting unknown network attacks is relatively lower,...Considering that growing hierarchical self-organizing map(GHSOM) ignores the influence of individual component in sample vector analysis, and its accurate rate in detecting unknown network attacks is relatively lower, an improved GHSOM method combined with mutual information is proposed. After theoretical analysis, experiments are conducted to illustrate the effectiveness of the proposed method by accurately clustering the input data. Based on different clusters, the complex relationship within the data can be revealed effectively.展开更多
Clustering is the main method of deinterleaving of radar pulse using multi-parameter.However,the problem in clustering of radar pulses lies in finding the right number of clusters.To solve this problem,a method is pro...Clustering is the main method of deinterleaving of radar pulse using multi-parameter.However,the problem in clustering of radar pulses lies in finding the right number of clusters.To solve this problem,a method is proposed based on Self-Organizing Feature Maps(SOFM) and Composed Density between and within clusters(CDbw).This method firstly extracts the feature of Direction Of Arrival(DOA) data by SOFM using the characteristic of DOA parameter,and then cluster of SOFM.Through computing the cluster validity index CDbw,the right number of clusters is found.The results of simulation show that the method is effective in sorting the data of DOA.展开更多
分布式拒绝服务(distributed denial of service,DDoS)攻击自出现以来一直是全球互联网网络安全的重要威胁之一。目前很多DDoS攻击检测方法虽然对已知类型攻击具有较高的检测率,但是不能有效识别新的攻击类型,无法应对DDoS攻击形式变化...分布式拒绝服务(distributed denial of service,DDoS)攻击自出现以来一直是全球互联网网络安全的重要威胁之一。目前很多DDoS攻击检测方法虽然对已知类型攻击具有较高的检测率,但是不能有效识别新的攻击类型,无法应对DDoS攻击形式变化多和快的特点。为了准确检测出DDoS攻击,同时使检测模型具有良好的自适应性、扩展性和较低的更新代价,以应对层出不穷的DDoS攻击,提出了一种综合考虑网络流量双向特征、固定特征和统计特征,采用增量式GHSOM(Growing Hierarchical Self-Organizing Maps)神经网络算法的DDoS攻击检测方法。首先,根据DDoS攻击流量的特点提取流量特征,组成流量八元组联合特征,然后利用增量式GHSOM神经网络算法进行异常流量分析,最后,通过实验验证检测方法的有效性。实验结果表明,提出的DDoS攻击检测方法不仅能够有效检测出已知类型的DDoS攻击,而且能够实现对检测模型的在线动态更新,对于新出现的DDoS攻击类型,具有相同的检测率。展开更多
The paper presents readily implementable approaches for fault detection and diagnosis （FDD） based on measurements from multiple sensor groups, for industrial systems. Specifically, the use of hierarchical clustering...The paper presents readily implementable approaches for fault detection and diagnosis （FDD） based on measurements from multiple sensor groups, for industrial systems. Specifically, the use of hierarchical clustering （HC） and self-organizing map neural networks （SOMNNs） are shown to provide robust and user-friendly tools for application to industrial gas turbine （IGT） systems. HC fingerprints are found for normal operation, and FDD is achieved by monitoring cluster changes occurring in the resulting dendrograms. Similarly, fingerprints of operational behaviour are also obtained using SOMNN based classification maps （CMs） that are initially determined during normal operation, and FDD is performed by detecting changes in their CMs. The proposed methods are shown to be capable of FDD from a large group of sensors that measure a variety of physical quantities. A key feature of the paper is the development of techniques to accommodate transient system operation, which can often lead to false-alarms being triggered when using traditional techniques if the monitoring algorithms are not first desensitized. Case studies showing the efficacy of the techniques for detecting sensor faults, bearing tilt pad wear and early stage pre-chamber burnout, are included. The presented techniques are now being applied operationally and monitoring IGTs in various regions of the world.展开更多
基金Supported by the Natural Science Foundation of Tianjin(No.15JCQNJC00200)
文摘Considering that growing hierarchical self-organizing map(GHSOM) ignores the influence of individual component in sample vector analysis, and its accurate rate in detecting unknown network attacks is relatively lower, an improved GHSOM method combined with mutual information is proposed. After theoretical analysis, experiments are conducted to illustrate the effectiveness of the proposed method by accurately clustering the input data. Based on different clusters, the complex relationship within the data can be revealed effectively.
文摘Clustering is the main method of deinterleaving of radar pulse using multi-parameter.However,the problem in clustering of radar pulses lies in finding the right number of clusters.To solve this problem,a method is proposed based on Self-Organizing Feature Maps(SOFM) and Composed Density between and within clusters(CDbw).This method firstly extracts the feature of Direction Of Arrival(DOA) data by SOFM using the characteristic of DOA parameter,and then cluster of SOFM.Through computing the cluster validity index CDbw,the right number of clusters is found.The results of simulation show that the method is effective in sorting the data of DOA.
文摘The paper presents readily implementable approaches for fault detection and diagnosis （FDD） based on measurements from multiple sensor groups, for industrial systems. Specifically, the use of hierarchical clustering （HC） and self-organizing map neural networks （SOMNNs） are shown to provide robust and user-friendly tools for application to industrial gas turbine （IGT） systems. HC fingerprints are found for normal operation, and FDD is achieved by monitoring cluster changes occurring in the resulting dendrograms. Similarly, fingerprints of operational behaviour are also obtained using SOMNN based classification maps （CMs） that are initially determined during normal operation, and FDD is performed by detecting changes in their CMs. The proposed methods are shown to be capable of FDD from a large group of sensors that measure a variety of physical quantities. A key feature of the paper is the development of techniques to accommodate transient system operation, which can often lead to false-alarms being triggered when using traditional techniques if the monitoring algorithms are not first desensitized. Case studies showing the efficacy of the techniques for detecting sensor faults, bearing tilt pad wear and early stage pre-chamber burnout, are included. The presented techniques are now being applied operationally and monitoring IGTs in various regions of the world.
