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Systems and methods for zero-footprint large-scale user-entity behavior modeling

A technology of behavior and storage space, applied in transmission systems, neural learning methods, biological neural network models, etc., can solve problems such as rapid increase, high cost, impractical storage of network flows, etc.

Pending Publication Date: 2020-09-01
EXTREME NETWORKS INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This is expensive because storing these records requires a lot of storage (many terabytes in total) and the real estate (i.e., "big data" facilities) to house that storage is equally expensive
This problem is exacerbated by the proliferation of gadgets and other devices becoming network-connected (e.g., Internet of Things devices), which multiplies the number of network endpoints to monitor, making it difficult for all It is impractical for network devices to store network flows

Method used

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  • Systems and methods for zero-footprint large-scale user-entity behavior modeling
  • Systems and methods for zero-footprint large-scale user-entity behavior modeling
  • Systems and methods for zero-footprint large-scale user-entity behavior modeling

Examples

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Embodiment Construction

[0027] figure 1 A system for reducing storage space used in tracking behavior of multiple network endpoints by modeling behavior with a behavior model is depicted in accordance with some embodiments of the present disclosure. Such as figure 1 As depicted in , server 100 is used to model the behavior of network endpoints (eg, network endpoint 112 and network endpoint 114 ) of network 110 . Although server 100 is described as one server comprising several components, this is for convenience only. Components of server 100 may be distributed across multiple servers and databases. As used herein, a network endpoint may be any end device, such as a consumer electronics device (eg, smartphone, personal computer, etc.), an Internet of Things device, or any other user-facing device connected to the network 110 .

[0028] Control circuitry 102 of server 100 receives records from network endpoints (eg, network device 112 and network device 114 ) of network 110 via communication circui...

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PUM

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Abstract

Systems and methods are disclosed herein for reducing storage space used in tracking behavior of a plurality of network endpoints by modeling the behavior with a behavior model. To this end, control circuitry may determine a respective network endpoint, of a plurality of network endpoints, to which each respective record of a plurality of received records corresponds. The control circuitry then may assign a dedicated queue for each respective network endpoint, and transmit, to each dedicated queue, each record that corresponds to the respective network endpoint to which the respective dedicated queue is assigned. The control circuitry may then determine, for each respective network endpoint, a respective behavior model, and may store each respective behavior model to memory.

Description

Background technique [0001] Traditionally, using a central server to monitor the network behavior of many network endpoint devices requires storing a large number of network flow records for analysis. For example, existing systems require Netflow and Internet Protocol Flow Information Output (“IPFIX”) records, Hypertext Transfer Protocol (“HTTP”) proxy logs, etc. to be stored in a “big data” backend for subsequent processing. This involves significant expense, as storing these records requires the use of large amounts of storage space (many terabytes in the aggregate) and the real estate (ie, "big data" facilities) to house this storage space is equally expensive. This problem is exacerbated by the proliferation of gadgets and other devices becoming network-connected (e.g., Internet of Things devices), which multiplies the number of network endpoints to be monitored, making all available for behavioral monitoring purposes. It is impractical for network devices to store networ...

Claims

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Application Information

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IPC IPC(8): G06F15/173
CPCH04L41/145H04L41/22G06N3/04G06F40/205H04L47/621H04L41/069H04L41/147H04L41/12G06N3/08H04L43/04G06F16/2255
Inventor G·博纳地M·尼斯佩尔
Owner EXTREME NETWORKS INC