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公开(公告)号：US11914462B2
公开(公告)日：2024-02-27
申请号：US18152590
申请日：2023-01-10
IPC分类号： G06F11/07 , G06N20/00 , G06F40/30 , G06F40/289
CPC分类号： G06F11/0781 , G06F40/289 , G06F40/30 , G06N20/00
摘要： Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.
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公开(公告)号：US12118084B2
公开(公告)日：2024-10-15
申请号：US17822158
申请日：2022-08-25
CPC分类号： G06F21/554 , G06F2221/034
摘要： Methods and systems are described herein for determining auxiliary parameters within datasets, the auxiliary parameters being used to segregate the datasets such that anomaly detection may be performed on the segregated datasets. Based on anomaly detection, alert conditions may then be identified. In particular, a system may, using a machine learning model, determine for a particular target feature (e.g., a parameter being monitored) one or more auxiliary features (other parameters) that effect the values of that parameter and transmit the target feature and the auxiliary features in a message to a monitoring system indicating which features to monitor. The collected data may then be received by the system and transformed into a timeseries dataset, which may then be used to detect anomalies within the data and thereby identify any anomalous points.
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公开(公告)号：US11977536B2
公开(公告)日：2024-05-07
申请号：US18189174
申请日：2023-03-23
CPC分类号： G06F16/2365 , G06F7/08
摘要： Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.
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公开(公告)号：US11640387B2
公开(公告)日：2023-05-02
申请号：US17238536
申请日：2021-04-23
摘要： Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.
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公开(公告)号：US12032538B2
公开(公告)日：2024-07-09
申请号：US17238486
申请日：2021-04-23
IPC分类号： G06F16/215 , G06F16/2458
CPC分类号： G06F16/215 , G06F16/2474
摘要： Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.
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公开(公告)号：US11579958B2
公开(公告)日：2023-02-14
申请号：US17239342
申请日：2021-04-23
IPC分类号： G06F11/07 , G06N20/00 , G06F40/30 , G06F40/289
摘要： Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.
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公开(公告)号：US20220342861A1
公开(公告)日：2022-10-27
申请号：US17239261
申请日：2021-04-23
发明人： Vannia Gonzalez Macias , Talha Koc , Mark Davis , Prarthana Bhattarai , Mark Roberts , Alan Rozet , Mengfei Shao
IPC分类号： G06F16/215 , G06F16/21
摘要： Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.
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公开(公告)号：US20220342745A1
公开(公告)日：2022-10-27
申请号：US17239342
申请日：2021-04-23
IPC分类号： G06F11/07 , G06F40/289 , G06F40/30 , G06N20/00
摘要： Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.
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公开(公告)号：US12079375B2
公开(公告)日：2024-09-03
申请号：US17824206
申请日：2022-05-25
IPC分类号： G06F21/64
CPC分类号： G06F21/64
摘要： Methods and systems are disclosed for using a feature hierarchy with multiple levels with a different number of features at different levels to segment a dataset. The mechanism may use the first level of the hierarchy to segment a dataset into multiple datasets and then generate a timeseries dataset for each segment. Those timeseries datasets may be input into an anomaly detection model to identify a number of anomalies detected within those segments. Based on the number of anomalies not reaching a threshold, a second level of the hierarchy may be used to segment the dataset. Those segments may be input into the anomaly detection model to determine a number of anomalies for the second level. This process may continue until a level of the hierarchy is determined such that the number of anomalies reaches the threshold. The mechanism may then generate a security rule to deal with the anomalies.
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公开(公告)号：US11856014B2
公开(公告)日：2023-12-26
申请号：US17239143
申请日：2021-04-23
发明人： Sarvani Kare , Vannia Gonzalez Macias , Farshid Marbouti , Stephen Fletcher , Boshika Tara , Patrick Sofo , Urvish Patel
IPC分类号： H04L9/40
CPC分类号： H04L63/1425 , H04L63/1416
摘要： Methods and systems are described herein for detecting anomalous access to system resources. An anomaly detection system may access system events from one or more computing devices and may generate entries from the system events. Each entry may include a corresponding timestamp indicating a time when a corresponding system event occurred, a corresponding user identifier indicating a user account within a computing environment associated with the corresponding system event, a corresponding location identifier indicating a location within the computing environment, and a corresponding action identifier indicating an action that the user account performed with respect to the location or an object within the computing environment. The generated entries may be aggregated and input into an anomaly detection model to obtain anomalous activity identified by the model.
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