US 11,910,249 B2Granted

Optimizing key allocation during roaming using machine learning

AssigneeHewlett Packard Enterprise Development LP

InventorsGopal Gupta, Abhinesh Mishra, Isaac Theogaraj, Sachin Ganu, Bernd Bandemer, Jose Tellado

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Abstract

Systems and methods are provided for optimizing resource consumption by bringing intelligence to the key allocation process for fast roaming. Specifically, embodiments of the disclosed technology use machine learning to predict which AP a wireless client device will migrate to next. In some embodiments, machine learning may also be used to select a subset of top neighbors from a neighborhood list. Thus, instead of allocating keys for each of the APs on the neighborhood list, key allocation may be limited to the predicted next AP, and the subset of top neighbors. In some embodiments, a reinforcement learning model may be used to dynamically adjust the size of the subset in order to optimize resources while satisfying variable client demand.

Claims20

Dependent claims

A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:

learning migration behavior associated with a client device;
receiving a notification that comprises a current access point and a neighborhood list, wherein the current access point is the access point the client device is currently connected to and the neighborhood list is a list of access points in the neighborhood of the current access point;
predicting an access point the client device will migrate to next based on the current access point and the learned client device migration behavior;
selecting a subset of neighbor access points from the neighborhood list based on the current access point and the learned client device migration behavior, wherein the size of the subset is dynamically adjusted using machine learning;
generating client keys for the predicted next access point and each of the subset of neighbor access points, wherein the client keys are cryptographic keys associated with the client device;
and
propagating one of the client keys to each of the predicted next access point and the subset of neighbor access points.

4 more elements

A system comprising:

at least one processor;
and
a memory storing instructions that, when executed by the at least one processor, cause the system to perform a method comprising:
learning migration behavior associated with a client device;
receiving a notification that comprises a current access point and a neighborhood list, wherein the current access point is the access point the client device is currently connected to and the neighborhood list is a list of access points in the neighborhood of the current access point;
predicting a subset of neighbor access points from the neighborhood list based on the current access point and the learned client device migration behavior, wherein the size of the subset is dynamically adjusted using machine learning;
and
generating client keys for the predicted next access point and each of the subset of neighbor access points.

5 more elements

A computer-implemented method comprising:

receiving, by a first access point, from a key management service, a cryptographic key associated with a client device, wherein the key management service operates to:
learn migration behavior associated with the client device;
receive a notification that comprises a second access point and a neighborhood list, wherein the second access point is the access point the client device is currently connected to and the neighborhood list is a list of access points in the neighborhood of the second access point;
predict a subset of neighbor access points from the neighborhood list based on the second access point and the learned client device migration behavior, wherein the size of the subset is dynamically adjusted using machine learning and the first access point is included in the subset;
generate cryptographic keys associated with the client device for each of the subset of neighbor access points;
and
propagating one of the cryptographic keys associated with the client device to each of subset of neighbor access points;
and
caching, by the first access point, the cryptographic key associated with the client device.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/391,559Filed 2 Aug 2021
Published: US 2023/0029760 A12 Feb 2023
Granted: 20 Feb 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification18 codes

H04W 36/0038
With transfer of context information: of security context information
G06N 3/0442
Recurrent networks, e.g. Hopfield networks: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
G06N 3/092
Learning methods: reinforcement learning
G06N 7/01
Computing arrangements based on specific mathematical models: probabilistic graphical models, e.g. probabilistic networks

Family

US 2023/0029760 A1Pre-grant publication · published 2 Feb 2023
US 11,910,249 B2This patent

From the US filing history. Foreign family members aren’t listed.

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