Identifying significant anomalous segments of a metrics dataset
First Claim
1. A computing system for detecting anomalous metrics data representing activity over a data network, the computing system comprising:
- a non-transitory computer-readable medium storing a metrics dataset comprising data items whose metrics values indicate data network activity, wherein the metrics dataset includes segments, wherein each segment comprises a respective subset of the data items having a common feature with respect to computing devices or users of the computing devices involved in the data network activity;
a processing device communicatively coupled to the non-transitory computer-readable medium and configured to access the metrics data set and to execute program code stored in the non-transitory computer-readable medium or another non-transitory computer-readable medium,wherein, when executed by the processing device, the program code configures the processing device to perform operations comprising;
identifying anomalous segments in the metrics dataset based on each anomalous segment having a respective segment trend that is different from a trend for the metrics dataset,generating a data graph comprising nodes and edges, wherein each node represents a respective one of the anomalous segments and is connected to the other nodes of the data graph via a respective subset of the edges,applying respective weights to the edges, wherein each weight applied to a respective edge between a respective pair of nodes is computed from (i) a respective similarity between a respective pair of anomalous segments represented by the nodes and (ii) a respective relationship between the respective pair of anomalous segments and the metrics dataset, wherein the operations further comprise determining each relationship between the respective pair of anomalous segments by performing additional operations comprising;
(a) computing a respective correlation score indicating a respective degree of correlation between the metrics dataset and at least one anomalous segment from the respective pair of anomalous segments,(b) computing a respective contribution score indicating a respective contribution of the at least one anomalous segment to the metrics values of the metrics dataset, and(c) determining the relationship from a respective combination of the respective correlation score and the respective contribution score,ranking the anomalous segments based on the weights applied to the edges, andselecting one of the anomalous segment having a rank that is greater than a threshold rank.
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Abstract
In some embodiments, a processor accesses a metrics dataset, which includes metrics whose values indicate data network activity. The metrics dataset has segments. Each segment is a respective subset of the data items having a common feature. The processor identifies anomalous segments in the metrics dataset. Each anomalous segment has a segment trend that is different from a trend associated with the larger metrics dataset. The processor generates a data graph that includes nodes, which represent anomalous segments, and edges connecting the nodes. The processor applies weights to the edges. Each weight indicates (i) a similarity between a pair of anomalous segments represented by the nodes connected by the weighted edge and (ii) a relationship between the anomalous segments and the metrics dataset. The processor ranks the anomalous segments based on the applied weights and selects one or more segments with sufficiently high ranks.
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Citations
17 Claims
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1. A computing system for detecting anomalous metrics data representing activity over a data network, the computing system comprising:
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a non-transitory computer-readable medium storing a metrics dataset comprising data items whose metrics values indicate data network activity, wherein the metrics dataset includes segments, wherein each segment comprises a respective subset of the data items having a common feature with respect to computing devices or users of the computing devices involved in the data network activity; a processing device communicatively coupled to the non-transitory computer-readable medium and configured to access the metrics data set and to execute program code stored in the non-transitory computer-readable medium or another non-transitory computer-readable medium, wherein, when executed by the processing device, the program code configures the processing device to perform operations comprising; identifying anomalous segments in the metrics dataset based on each anomalous segment having a respective segment trend that is different from a trend for the metrics dataset, generating a data graph comprising nodes and edges, wherein each node represents a respective one of the anomalous segments and is connected to the other nodes of the data graph via a respective subset of the edges, applying respective weights to the edges, wherein each weight applied to a respective edge between a respective pair of nodes is computed from (i) a respective similarity between a respective pair of anomalous segments represented by the nodes and (ii) a respective relationship between the respective pair of anomalous segments and the metrics dataset, wherein the operations further comprise determining each relationship between the respective pair of anomalous segments by performing additional operations comprising; (a) computing a respective correlation score indicating a respective degree of correlation between the metrics dataset and at least one anomalous segment from the respective pair of anomalous segments, (b) computing a respective contribution score indicating a respective contribution of the at least one anomalous segment to the metrics values of the metrics dataset, and (c) determining the relationship from a respective combination of the respective correlation score and the respective contribution score, ranking the anomalous segments based on the weights applied to the edges, and selecting one of the anomalous segment having a rank that is greater than a threshold rank. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method comprising:
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a step for accessing a metrics dataset comprising data items whose metrics values indicate data network activity, wherein the metrics dataset includes segments, wherein each segment comprises a respective subset of the data items having a common feature with respect to computing devices or users of the computing devices involved in the data network activity; a step for identifying anomalous segments in the metrics dataset; a step for generating a data graph comprising nodes and edges, wherein each node represents a respective one of the anomalous segments and is connected to the other nodes of the data graph via a respective subset of the edges; a step for applying respective weights to the edges, wherein each weight applied to a respective edge between a respective pair of nodes is computed from (i) a respective similarity between a respective pair of anomalous segments represented by the nodes and (ii) a respective relationship between the respective pair of anomalous segments and the metrics dataset, wherein the method further comprises determining each relationship between the respective pair of anomalous segments by performing operations comprising; (a) computing a respective correlation score indicating a respective degree of correlation between the metrics dataset and at least one anomalous segment from the respective pair of anomalous segments, (b) computing a respective contribution score indicating a respective contribution of the at least one anomalous segment to the metrics values of the metrics dataset, and (c) determining the relationship from a respective combination of the respective correlation score and the respective contribution score; a step for ranking the anomalous segments based on the weights applied to the edges; and a step for selecting one of the anomalous segment having a rank that is greater than a threshold rank. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A non-transitory computer-readable medium having program code executable by a processing device stored thereon, the program code comprising:
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program code for accessing a metrics dataset comprising data items whose metrics values indicate data network activity, wherein the metrics dataset includes segments, wherein each segment comprises a respective subset of the data items having a common feature with respect to computing devices or users of the computing devices involved in the data network activity; program code for identifying anomalous segments in the metrics dataset based on each anomalous segment having a respective segment trend that is different from a trend for the metrics dataset; program code for generating a data graph comprising nodes and edges, wherein each node represents a respective one of the anomalous segments and is connected to the other nodes of the data graph via a respective subset of the edges; program code for applying respective weights to the edges, wherein each weight applied to a respective edge between a respective pair of nodes is computed from (i) a respective similarity between a respective pair of anomalous segments represented by the nodes and (ii) a respective relationship between the respective pair of anomalous segments and the metrics dataset, wherein the program code further comprises program code for determining each relationship between the respective pair of anomalous segments by performing operations comprising; (a) computing a respective correlation score indicating a respective degree of correlation between the metrics dataset and at least one anomalous segment from the respective pair of anomalous segments, (b) computing a respective contribution score indicating a respective contribution of the at least one anomalous segment to the metrics values of the metrics dataset, and (c) determining the relationship from a respective combination of the respective correlation score and the respective contribution score, program code for ranking the anomalous segments based on the weights applied to the edges; and program code for selecting one of the anomalous segment having a rank that is greater than a threshold rank. - View Dependent Claims (14, 15, 16, 17)
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Specification