Classifying access point beacon communications using machine learning
AssigneeSilicon Laboratories Inc.
InventorKrishna Chaitanya Suryavenkata Emani
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Abstract
In one aspect, a method comprises: receiving, in a machine learning (ML) classifier, power plot information associated with communication between a wireless device and an access point; analyzing, in the ML classifier, the power plot information comprising determining whether an anomalous condition exists with respect to beacon communication between the wireless device and the access point; and in response to determining the anomalous condition, classifying a type of an anomalous beacon communication.
Claims20
A computer readable medium comprising instructions that, when executed, cause a system to perform a method comprising:
- receiving, in a machine learning (ML) classifier, power plot information associated with communication between a wireless device and an access point;
- analyzing, in the ML classifier, the power plot information comprising determining whether an anomalous condition exists with respect to beacon communication between the wireless device and the access point;
- and
- in response to determining the anomalous condition, classifying a type of an anomalous beacon communication.
A method comprising:
- configuring a wireless device to operate in a standby mode;
- in the standby mode, receiving a plurality of beacons from an access point in a wireless network with the wireless device;
- while the wireless device is in the standby mode, measuring a power consumption of the wireless device during a first duration;
- and
- providing the measured power consumption of the wireless device to a power analyzer to cause generation of power plot information of the power consumption and communication of the power plot information to a machine learning (ML) classifier.
A system comprising:
- transceiver circuitry to receive radio frequency (RF) signals, the RF signals comprising beacon communications from an access point;
- a power monitor coupled to the transceiver circuitry to measure a power consumption of the beacon communications during a first duration;
- a controller coupled to the power monitor, the controller to generate power plot information based on the measured power consumption;
- a machine learning (ML) classifier to identify whether an anomalous condition exists with respect to the beacon communications;
- and
- a configuration circuit coupled to the ML classifier, wherein the configuration circuit is to update at least one configuration setting of the transceiver circuitry in response to an identification of the anomalous condition.
Dependencies are inferred from the claim text.