US Patent Application 18020909. ANOMALOUS PATTERN DETECTION FOR CONTROL OF COMPUTER NETWORKS simplified abstract
Contents
ANOMALOUS PATTERN DETECTION FOR CONTROL OF COMPUTER NETWORKS
Organization Name
MICROSOFT TECHNOLOGY LICENSING, LLC
Inventor(s)
Ananth Geethanath of Bothell WA (US)
Shankaranand Arunachalam of Redmond WA (US)
ANOMALOUS PATTERN DETECTION FOR CONTROL OF COMPUTER NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18020909 titled 'ANOMALOUS PATTERN DETECTION FOR CONTROL OF COMPUTER NETWORKS
Simplified Explanation
The patent application describes a system and method for detecting anomalies in a data stream.
- The system receives a data stream that contains values of metrics derived from observations of a computing entity's operation over a specific time window.
- A model is created using the variances of the data over the time window, which helps identify operating thresholds for each metric in the data stream.
- The system computes a steady state distance matrix of the data stream and determines if it exceeds a steady state threshold.
- If the steady state distance matrix exceeds the threshold, the system computes a pattern distance matrix based on the steady state distance matrix.
- The anomaly in the data stream is detected based on the pattern distance matrix.
- Finally, the system generates an alert to indicate the presence of an anomaly.
Original Abstract Submitted
A system and method for detecting anomalies in a data stream is described. The system receives the data stream that comprises values of metrics derived from observations of operation of a computing entity over a time window. A model comprising variances of the data over the time window is formed. The model identifies operating thresholds for each metric based on the variances of the data for each metric in the data stream. The system computes a steady state distance matrix of the data stream. The system determines that the steady state distance matrix exceeds a steady state threshold. In response to determining that the steady state distance matrix exceeds the steady state threshold, the system computes a pattern distance matrix based on the steady state distance matrix. The anomaly in the data stream is detected based on the pattern distance matrix. The system generates an alert indicating the anomaly.
