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1. 发明申请

US20200233774A1 System and Method for Efficient Estimation of High Cardinality Time-Series Models 审中-公开
公开(公告)号：US20200233774A1
公开(公告)日：2020-07-23
申请号：US16703762
申请日：2019-12-04
申请人： Anodot Ltd.
发明人： Meir TOLEDANO
IPC分类号： G06F11/34 , G06F16/28 , G06F11/30
摘要： A system includes a metric data store configured to receive and store a time-series of values of a first metric, a seasonal trend identification module configured to determine a periodicity profile for the first metric, and a modeling module configured to generate an autoregressive moving average (ARMA) model. The modeling module includes a seasonal model module configured to generate a first model of the time-series of values, a non-seasonal model module configured to generate a second model of the time-series of values, and a combination module configured to generate a third model based on the first and second models. The modeling module is configured to, in response to determining that a first periodicity profile describes the time-series of values, output the third model as the ARMA model. The system includes an envelope determination module configured to determine a normal behavior of the first metric based on the ARMA model.

2. 发明申请

US20160147583A1 System and Method for Transforming Observed Metrics into Detected and Scored Anomalies 审中-公开
标题翻译：将观察到的指标变换为检测和计分异常的系统和方法
公开(公告)号：US20160147583A1
公开(公告)日：2016-05-26
申请号：US14950357
申请日：2015-11-24
申请人： Anodot Ltd.
发明人： Yoni Yom Tov BEN SIMHON , David DRAI , Ira COHEN
IPC分类号： G06F11/07
CPC分类号： G06F11/076 , G06F11/0721 , G06F11/079 , G06F11/34 , G06F11/3447 , G06F11/3452 , H04L43/08 , H04L43/0823 , H04L43/16
摘要： A system includes a normal behavior characterization module configured to receive values for a first metric of a plurality of metrics and generate a baseline profile indicating normal behavior of the first metric based on the received values. The system also includes an anomaly identification module configured to identify an anomaly in response to present values of the metric deviating outside the baseline profile. The system also includes an anomaly behavior characterization module configured to analyze a plurality of prior anomalies identified by the anomaly identification module and develop a model of the anomalies of the first metric. The system also includes an anomaly scoring module configured to determine a first score for a present anomaly detected by the anomaly identification module for the first metric. The first score is based on characteristics of the present anomaly and the model of the anomalies of the first metric.
摘要翻译：系统包括正常行为表征模块，其被配置为接收多个度量的第一度量的值，并且基于接收到的值生成指示第一度量的正常行为的基线简档。该系统还包括异常识别模块，该异常识别模块被配置为响应于偏离基线轮廓的度量的当前值来识别异常。该系统还包括异常行为表征模块，其被配置为分析由异常识别模块识别的多个先前的异常并且开发第一度量的异常的模型。系统还包括异常评分模块，其被配置为确定由异常识别模块检测到的针对第一度量的当前异常的第一分数。第一个分数是基于当前异常的特征和第一个度量的异常模型。

3. 发明申请

US20180365090A1 System and Method for Transforming Observed Metrics into Detected and Scored Anomalies 审中-公开
公开(公告)号：US20180365090A1
公开(公告)日：2018-12-20
申请号：US16113468
申请日：2018-08-27
申请人： Anodot Ltd.
发明人： Yoni Yom Tov BEN SIMHON , David DRAI , Ira COHEN
IPC分类号： G06F11/07 , H04L12/26
摘要： A system includes a normal behavior characterization module configured to receive values for a first metric of a plurality of metrics and generate a baseline profile indicating normal behavior of the first metric based on the received values. The system also includes an anomaly identification module configured to identify an anomaly in response to present values of the metric deviating outside the baseline profile. The system also includes an anomaly behavior characterization module configured to analyze a plurality of prior anomalies identified by the anomaly identification module and develop a model of the anomalies of the first metric. The system also includes an anomaly scoring module configured to determine a first score for a present anomaly detected by the anomaly identification module for the first metric. The first score is based on characteristics of the present anomaly and the model of the anomalies of the first metric.

4. 发明申请

US20180136994A1 Fast Automated Detection of Seasonal Patterns in Time Series Data Without Prior Knowledge of Seasonal Periodicity 审中-公开
公开(公告)号：US20180136994A1
公开(公告)日：2018-05-17
申请号：US15353709
申请日：2016-11-16
申请人： Anodot Ltd.
发明人： Meir TOLEDANO
IPC分类号： G06F11/07
CPC分类号： G06F11/3072 , G06F11/0721 , G06F11/079 , G06F11/321 , G06F11/322 , G06F11/327 , G06F11/3428
摘要： A processing system receives a time series of values of a first metric corresponding to computing system performance. A computation module calculates an autocorrelation function (ACF) based on the time series of values across a set of values of tau. The spacing between each consecutive pair of values in the set of values of tau increases as tau increases. A local maxima extraction module identifies local maxima of the calculated ACF. A period determination module determines a significant period based on spacing between the local maxima and selectively outputs the significant period as a periodicity profile. A baseline profile indicating normal behavior of the first metric is generated based on the periodicity profile. An anomaly identification module selectively identifies an anomaly in present values of the first metric in response to the present values deviating outside the baseline profile.

5. 发明授权

US11030032B2 System and method for transforming observed metrics into detected and scored anomalies 有权
公开(公告)号：US11030032B2
公开(公告)日：2021-06-08
申请号：US16113468
申请日：2018-08-27
申请人： Anodot Ltd.
发明人： Yoni Yom Tov Ben Simhon , David Drai , Ira Cohen
IPC分类号： G06F11/34 , G06F11/07 , H04L12/26
摘要： A system includes a normal behavior characterization module configured to receive values for a first metric of a plurality of metrics and generate a baseline profile indicating normal behavior of the first metric based on the received values. The system also includes an anomaly identification module configured to identify an anomaly in response to present values of the metric deviating outside the baseline profile. The system also includes an anomaly behavior characterization module configured to analyze a plurality of prior anomalies identified by the anomaly identification module and develop a model of the anomalies of the first metric. The system also includes an anomaly scoring module configured to determine a first score for a present anomaly detected by the anomaly identification module for the first metric. The first score is based on characteristics of the present anomaly and the model of the anomalies of the first metric.

6. 发明授权

US10061677B2 Fast automated detection of seasonal patterns in time series data without prior knowledge of seasonal periodicity 有权
公开(公告)号：US10061677B2
公开(公告)日：2018-08-28
申请号：US15353709
申请日：2016-11-16
申请人： Anodot Ltd.
发明人： Meir Toledano
IPC分类号： G06F11/30 , G06F11/32 , G06F11/34 , G06F11/07
CPC分类号： G06F11/3072 , G06F11/0721 , G06F11/079 , G06F11/321 , G06F11/322 , G06F11/327 , G06F11/3428
摘要： A processing system receives a time series of values of a first metric corresponding to computing system performance. A computation module calculates an autocorrelation function (ACF) based on the time series of values across a set of values of tau. The spacing between each consecutive pair of values in the set of values of tau increases as tau increases. A local maxima extraction module identifies local maxima of the calculated ACF. A period determination module determines a significant period based on spacing between the local maxima and selectively outputs the significant period as a periodicity profile. A baseline profile indicating normal behavior of the first metric is generated based on the periodicity profile. An anomaly identification module selectively identifies an anomaly in present values of the first metric in response to the present values deviating outside the baseline profile.

7. 发明授权

US10061632B2 System and method for transforming observed metrics into detected and scored anomalies 有权
公开(公告)号：US10061632B2
公开(公告)日：2018-08-28
申请号：US14950357
申请日：2015-11-24
申请人： Anodot Ltd.
发明人： Yoni Yom Tov Ben Simhon , David Drai , Ira Cohen
IPC分类号： G06F11/34 , G06F11/07 , H04L12/26
CPC分类号： G06F11/076 , G06F11/0721 , G06F11/079 , G06F11/34 , G06F11/3447 , G06F11/3452 , H04L43/08 , H04L43/0823 , H04L43/16
摘要： A system includes a normal behavior characterization module configured to receive values for a first metric of a plurality of metrics and generate a baseline profile indicating normal behavior of the first metric based on the received values. The system also includes an anomaly identification module configured to identify an anomaly in response to present values of the metric deviating outside the baseline profile. The system also includes an anomaly behavior characterization module configured to analyze a plurality of prior anomalies identified by the anomaly identification module and develop a model of the anomalies of the first metric. The system also includes an anomaly scoring module configured to determine a first score for a present anomaly detected by the anomaly identification module for the first metric. The first score is based on characteristics of the present anomaly and the model of the anomalies of the first metric.

8. 发明授权

US11341019B2 System and method for efficient estimation of high cardinality time-series models 有权
公开(公告)号：US11341019B2
公开(公告)日：2022-05-24
申请号：US16703762
申请日：2019-12-04
申请人： Anodot Ltd.
发明人： Meir Toledano
IPC分类号： G06F11/34 , G06F11/30 , G06F16/28
摘要： A system includes a metric data store configured to receive and store a time-series of values of a first metric, a seasonal trend identification module configured to determine a periodicity profile for the first metric, and a modeling module configured to generate an autoregressive moving average (ARMA) model. The modeling module includes a seasonal model module configured to generate a first model of the time-series of values, a non-seasonal model module configured to generate a second model of the time-series of values, and a combination module configured to generate a third model based on the first and second models. The modeling module is configured to, in response to determining that a first periodicity profile describes the time-series of values, output the third model as the ARMA model. The system includes an envelope determination module configured to determine a normal behavior of the first metric based on the ARMA model.

9. 发明申请

US20210089917A1 Heuristic Inference of Topological Representation of Metric Relationships 有权
公开(公告)号：US20210089917A1
公开(公告)日：2021-03-25
申请号：US17114529
申请日：2020-12-08
申请人： Anodot Ltd.
发明人： Yoni Yom Tov Ben SIMHON , Ira COHEN
IPC分类号： G06N3/08 , G06N20/00 , H04L29/06 , G06F11/34 , G06F11/07
摘要： A system includes a windowing module that divides time series data for each metric into portions. Each portion corresponds to a respective window of time. A hash module calculates a hash value for each of the portions for each of the metrics. An identification module compares the hash values for each pair of metrics and, for a selected pair of metrics, counts how many windows of time in which the hash values of the selected pair of metrics are equal. A pair is identified as a candidate pair in response to the count exceeding a threshold. A metric graph module creates a first edge in a graph based on the candidate pair of metrics. Each of the metrics is a node in the graph and direct relationships between each pair of the metrics are edges in the graph. An anomaly combination module analyzes an anomaly condition based on the graph.

10. 发明授权

US10891558B2 Creation of metric relationship graph based on windowed time series data for anomaly detection 有权
公开(公告)号：US10891558B2
公开(公告)日：2021-01-12
申请号：US15002260
申请日：2016-01-20
申请人： Anodot Ltd.
发明人： Yoni Yom Tov Ben Simhon , Ira Cohen
IPC分类号： G06N20/00 , G06N3/08 , G06F11/34 , G06F11/07 , H04L29/06
摘要： A system includes a windowing module that divides time series data for each metric into portions. Each portion corresponds to a respective window of time. A hash module calculates a hash value for each of the portions for each of the metrics. An identification module compares the hash values for each pair of metrics and, for a selected pair of metrics, counts how many windows of time in which the hash values of the selected pair of metrics are equal. A pair is identified as a candidate pair in response to the count exceeding a threshold. A metric graph module creates a first edge in a graph based on the candidate pair of metrics. Each of the metrics is a node in the graph and direct relationships between each pair of the metrics are edges in the graph. An anomaly combination module analyzes an anomaly condition based on the graph.

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