Varonis Systems has been granted a patent for a method to detect and handle anomalies in a network. The method involves collecting meta-data, extracting features, clustering organizations, training models, analyzing event data, providing decision scores, and handling anomalies based on the scores. GlobalData’s report on Varonis Systems gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on Varonis Systems, Quantum key distribution was a key innovation area identified from patents. Varonis Systems's grant share as of April 2024 was 53%. Grant share is based on the ratio of number of grants to total number of patents.
Method for detecting and handling anomalies in a network

A recently granted patent (Publication Number: US11914461B1) discloses a method and system for detecting and handling anomalies in a network. The method involves collecting meta-data related to the environment of each organization using the network, extracting features from the meta-data, clustering organizations with common features into segments, collecting training data, grouping the data according to segments, training a model for each segment to detect anomalies, analyzing event data with the respective model, providing a decision score, and handling the anomaly based on the score. The trained models can be delivered to local computers within organizations or stored at a multi-tenant server for analyzing event data across segments.
Furthermore, the system includes a central server computer with a processor and memory, executing a program to collect meta-data, extract features, cluster organizations, collect training data, train models, analyze event data, provide decision scores, and handle anomalies. The decision score determines the priority of handling an anomaly, with the model increasing the score for simultaneous anomalies within the same segment. Clustering is periodically repeated to form new clusters, triggering model retraining and reevaluation of previous events. The system aims to efficiently detect and handle anomalies in a network by utilizing meta-data from various sources and training models specific to segments for effective anomaly detection and response.
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