公开(公告)号：US20190147343A1

公开(公告)日：2019-05-16

申请号：US15813192

申请日：2017-11-15

申请人： International Business Machines Corporation

发明人： GUY LEV ,  Matan Ninio ,  Oren Sar Shalom

IPC分类号： G06N3/08 ,  G06N3/04 ,  G06F7/02 ,  G06F17/18

摘要： A method, system and computer program product, the method comprising: mutually training, using feedback, a generator and a discriminator of a conditional adversarial generative adversarial networks using training item groups, each item group representing events in a time window, the generator comprises a generator Recurrent Neural Network (RNN), the discriminator comprises a discriminator RNN; receiving by the discriminator, discrete sequential data comprising a sequence of item groups comprising an item group representing events in a time window, and item groups representing events in preceding time windows; altering the sequence of item groups into collections of real numbers and providing them to the discriminator RNN; processing the collections by the discriminator RNN to obtain a probability for the item group to comprise an anomaly, in an unsupervised manner; and providing output to a user, the output based on the probability and indicative of a label for the discrete sequential data.

公开(公告)号：US09245233B2

公开(公告)日：2016-01-26

申请号：US13947126

申请日：2013-07-22

申请人： International Business Machines Corporation

发明人： Yaara Goldschmidt ,  Ofer Lavi ,  Matan Ninio

IPC分类号： G06F15/18 ,  G06N99/00 ,  G06N7/00

CPC分类号： G06N99/005 ,  G06F17/30958 ,  G06N7/005

摘要： A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.

摘要翻译： 用于自动检测图中异常的方法，设备和产品。 所述方法包括获得训练数据，训练数据包括多个图，每个图由节点和连接节点之间的边缘定义，至少一些节点被标记; 根据训练数据确定图形的统计模型，所述统计模型考虑到所述图形的至少一个结构化和标记特征，其中所述图形的所述结构化和标记特征基于多个 并且基于所述多个节点的标签的至少一部分; 获得检查图; 以及确定所检查的图表的得分，其指示所检查的图和训练数据之间的相似性，其中所述分数基于所检查的图中的结构化和标记的特征的值。

公开(公告)号：US20160004978A1

公开(公告)日：2016-01-07

申请号：US14839981

申请日：2015-08-30

申请人： International Business Machines Corporation

发明人： Yaara Goldschmidt ,  Ofer Lavi ,  Matan Ninio

IPC分类号： G06N99/00 ,  G06F17/30

CPC分类号： G06N7/005 ,  G06F16/9024 ,  G06F16/9027 ,  G06N20/00

摘要： A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.

公开(公告)号：US20150026103A1

公开(公告)日：2015-01-22

申请号：US13947126

申请日：2013-07-22

申请人： International Business Machines Corporation

发明人： Yaara Goldschmidt ,  Ofer Lavi ,  Matan Ninio

IPC分类号： G06N99/00

CPC分类号： G06N99/005 ,  G06F17/30958 ,  G06N7/005

摘要： A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.

摘要翻译： 用于自动检测图中异常的方法，装置和产品。 所述方法包括获得训练数据，训练数据包括多个图，每个图由节点和连接节点之间的边缘定义，至少一些节点被标记; 根据训练数据确定图形的统计模型，所述统计模型考虑到所述图形的至少一个结构化和标记特征，其中所述图形的所述结构化和标记特征基于多个 并且基于所述多个节点的标签的至少一部分; 获得检查图; 以及确定所检查的图表的得分，其指示所检查的图和训练数据之间的相似性，其中所述分数基于所检查的图中的结构化和标记的特征的值。