View source for 18480318. DISTRIBUTED MACHINE LEARNING SOLUTION FOR ROGUE BASE STATION DETECTION simplified abstract (Nokia Technologies Oy)

=DISTRIBUTED MACHINE LEARNING SOLUTION FOR ROGUE BASE STATION DETECTION=


==Organization Name==

[[:Category:Nokia Technologies Oy|Nokia Technologies Oy]]


[[Category:Nokia Technologies Oy]]

==Inventor(s)==

[[:Category:Serge Papillon of Massy (FR)|Serge Papillon of Massy (FR)]][[Category:Serge Papillon of Massy (FR)]]

[[:Category:Afef Feki of Massy (FR)|Afef Feki of Massy (FR)]][[Category:Afef Feki of Massy (FR)]]

[[:Category:Koffi Ismael Ouattara of Nozay (FR)|Koffi Ismael Ouattara of Nozay (FR)]][[Category:Koffi Ismael Ouattara of Nozay (FR)]]

[[:Category:Anna Pantelidou of Massy (FR)|Anna Pantelidou of Massy (FR)]][[Category:Anna Pantelidou of Massy (FR)]]

==DISTRIBUTED MACHINE LEARNING SOLUTION FOR ROGUE BASE STATION DETECTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18480318 titled 'DISTRIBUTED MACHINE LEARNING SOLUTION FOR ROGUE BASE STATION DETECTION

==Simplified Explanation==

The patent application describes an apparatus that can distribute partitions of a machine learning model to different user equipments, record measurements for a cell, receive messages about handover failures, and determine if a base station is rogue based on detection reports.

* The apparatus distributes partitions of a machine learning model to user equipments.
* It records measurements for a cell and receives messages about handover failures.
* It uses machine learning model partitions to determine if a base station is rogue based on detection reports.

== Potential Applications ==
This technology could be applied in telecommunications networks to improve handover processes and detect rogue base stations.

== Problems Solved ==
This technology helps in optimizing handover processes in cellular networks and enhances security by identifying rogue base stations.

== Benefits ==
The benefits of this technology include improved network efficiency, enhanced security measures, and better overall performance of cellular networks.

== Potential Commercial Applications ==
One potential commercial application of this technology could be in the telecommunications industry for network optimization and security enhancement.

== Possible Prior Art ==
One possible prior art could be related to machine learning models for network optimization and security in telecommunications systems.

== Unanswered Questions ==
=== How does the apparatus handle handover failures effectively? ===
The apparatus receives messages about handover failures, but the specific methods for handling these failures are not detailed in the abstract.

=== What types of detection reports are used to determine if a base station is rogue? ===
The abstract mentions using a second partition of the machine learning model to determine if a base station is rogue, but it does not specify the exact types of detection reports used for this purpose.


==Original Abstract Submitted==

An apparatus configured to: obtain an indication of partitions of a machine learning model corresponding to respective ones of the one or more groups; transmit, to the respective ones of the plurality of user equipments, a corresponding partition, of the partitions of the machine learning model; transmit, to the plurality of user equipments, an indication to record measurements for the first cell; receive, from at least one of the plurality of user equipments, at least one message regarding a handover failure, wherein the at least one message comprises a message generated using a first partition of the partitions of the machine learning model; and determine, with a second partition of the partitions of the machine learning model, whether the first cell is a rogue base station based, at least partially, on a plurality of detection reports.



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