Cold start mechanism to prevent compromise of automatic anomaly detection systems
First Claim
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1. A method, comprising:
- analyzing, by a device in a network, data indicative of a behavior of the network using a supervised anomaly detection model;
prior to training an unsupervised anomaly detection model, validating, by the device, that the network has not already been compromised by determining, whether the supervised anomaly detection model has detected an anomaly in the network from the analyzed data;
in response to determining that the supervised anomaly detection model has detected an anomaly in the network, suspending, by the device, training of an unsupervised anomaly detection model, wherein the supervised anomaly detection model is used to quantify the behavior of the network prior to training the unsupervised anomaly detection model; and
in response to determining that no anomalies were detected by the supervised anomaly detection model, training, by the device, the unsupervised anomaly detection model.
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Abstract
In one embodiment, a device in a network analyzes data indicative of a behavior of a network using a supervised anomaly detection model. The device determines whether the supervised anomaly detection model detected an anomaly in the network from the analyzed data. The device trains an unsupervised anomaly detection model, based on a determination that no anomalies were detected by the supervised anomaly detection model.
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Citations
20 Claims
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1. A method, comprising:
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analyzing, by a device in a network, data indicative of a behavior of the network using a supervised anomaly detection model; prior to training an unsupervised anomaly detection model, validating, by the device, that the network has not already been compromised by determining, whether the supervised anomaly detection model has detected an anomaly in the network from the analyzed data; in response to determining that the supervised anomaly detection model has detected an anomaly in the network, suspending, by the device, training of an unsupervised anomaly detection model, wherein the supervised anomaly detection model is used to quantify the behavior of the network prior to training the unsupervised anomaly detection model; and in response to determining that no anomalies were detected by the supervised anomaly detection model, training, by the device, the unsupervised anomaly detection model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. An apparatus, comprising:
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one or more network interfaces to communicate with a network; a processor coupled to the network interfaces and adapted to execute one or more processes; and a memory configured to store a process executable by the processor, the process when executed configured to; analyze data indicative of a behavior of the network using a supervised anomaly detection model; prior to training an unsupervised anomaly detection model, validating, that the network has not already been compromised by determining whether the supervised anomaly detection model has detected an anomaly in the network from the analyzed data; in response to determining that the supervised anomaly detection model has detected an anomaly in the network, suspend training of the unsupervised anomaly detection model, wherein the supervised anomaly detection model is used to quantify the behavior of the network prior to training the unsupervised anomaly detection model; and in response to determining that no anomalies were detected by the supervised anomaly detection model, train the unsupervised anomaly detection model. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. A tangible, non-transitory, computer-readable media having software encoded thereon, the software when executed by a processor configured to:
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analyze data indicative of a behavior of a network using a supervised anomaly detection model; prior to training an unsupervised anomaly detection model, validating, that the network has not already been compromised by determining whether the supervised anomaly detection model has detected an anomaly in the network from the analyzed data; in response to determining that the supervised anomaly detection model has detected an anomaly in the network, suspend training of the unsupervised anomaly detection model, wherein the supervised anomaly detection model is used to quantify the behavior of the network prior to training the unsupervised anomaly detection model; and in response to determining that no anomalies were detected by the supervised anomaly detection model, train the unsupervised anomaly detection model. - View Dependent Claims (17, 18, 19, 20)
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Specification