Extreme Networks has patented a method to reduce storage space in tracking network endpoint behavior by utilizing behavior models. The method involves encoding network flow data, generating behavior models using a doc2vec algorithm, and detecting anomalous behavior states. This innovation allows for efficient monitoring and analysis of network endpoints. GlobalData’s report on Extreme Networks gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Extreme Networks, VPN tunneling was a key innovation area identified from patents. Extreme Networks's grant share as of May 2024 was 63%. Grant share is based on the ratio of number of grants to total number of patents.
Reducing storage space for tracking network endpoint behavior
A recently granted patent (Publication Number: US11996986B2) discloses a method and system for reducing storage space used in tracking the behavior of a network endpoint. The method involves determining a record corresponding to a single network flow, extracting a subset of fields from the record, encoding them into a flow word, and generating an endpoint vector representing the behavior model for the network endpoint. This behavior model is stored in memory and used to determine anomalous behavior states by comparing the endpoint vector to a normalcy threshold in a multidimensional space. The tracking of the network endpoint's behavior over time is achieved by comparing the current position of the endpoint vector to a previous version in the multidimensional space. The system includes storage, communications, and control circuitry to perform these operations efficiently.
Furthermore, the system and method include additional features such as detecting unknown network endpoints, generating behavior models based on accumulated flow words, identifying idle modules to generate endpoint vectors, and utilizing a Document to Vector (doc2vec) algorithm for analysis. The tracking process involves using a Kalman filter and deriving a multivariate Gaussian distribution to determine the current position of the endpoint vector. The patent also covers a non-transitory computer-readable medium with instructions for executing these operations on a computing device. Overall, the patent aims to optimize storage space while effectively tracking and analyzing the behavior of network endpoints in a multidimensional space, providing a comprehensive solution for network monitoring and anomaly detection.
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