Arris Enterprises LLC

Micro Focus Lcc

Lakshmi Nagarajan

Yang Han

Jiayi Chen

See Ho Ting

Shi Xing Yan

Ilango Purushothaman

Koteswar Rao Mellachervu

SeeHo Ting

Han Ju

During operation, a computer may compare values of at least one performance metric for access points in appropriate contexts to determine one or more temporal anomalies and/or one or more spatial anomalies for one or more of the access points. Then, the computer may generate one or more temporal anomaly events based at least in part on the one or more temporal anomalies and one or more spatial anomaly events based at least in part on the one or more spatial anomalies. Next, the computer may calculate one or more complex events based at least in part on two or more of the different anomalies. Moreover, the computer may evaluate the different anomalies, anomaly event and/or complex events to determine one or more insights about a problem in the network. Furthermore, the computer may perform a remedial action.

Access point performance monitoring and anomaly detection based on temporal and spatial anomalies

Automatic detection of outliers in multivariate data is disclosed. One example is a system including a distance generator to determine Mahalanobis distances for data elements of multivariate input data, the Mahalanobis distance of a given data element indicative of a measure of distance of the given data element from a probability distribution associated with the input data. A filter module selects a portion of the input data, the cohesive portion indicative of cohesive data elements, and the selection based on minimizing the Mahalanobis distances, and identifies candidate outliers to be data elements not in the cohesive portion. An outlier detector automatically detects outliers of the candidate outliers, the detection based on median absolute deviations of the Mahalanobis distances of the input data, and a modified z-score. A display module generates a visual representation of the detected outliers.

Automatic detection of outliers in multivariate data

A method includes receiving an alert representing that application of at least one rule to a scenario occurring with a financial account indicates that the scenario is consistent with illegal activity. The method includes, in response to the alert, using a fuzzy inference derived from activity of the financial account to determine a likelihood that the scenario is associated with illegal activity.

Using fuzzy inference to determine likelihood that financial account scenario is associated with illegal activity

During operation, a computer may monitor values of at least one performance metric associated with packet communication by access points in a network. Then, the computer may compare current values of the performance metric for the access points with historical values of the performance metric for the access points within a temporal context in order to determine temporal anomaly scores for the access points. Moreover, the computer may compare the temporal anomaly scores of a group of access points in order to determine spatial temporal anomaly scores for the access points that indicate a significance of the temporal anomaly scores in a spatial context. Furthermore, when a spatial temporal anomaly score for an access point meets a detection criterion, the computer system may identify the current value of the performance metric for the access point as an anomaly. Next, the computer system may perform a remedial action based on the identified anomaly.

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Singapore, Singapore

Lakshmi Nagarajan