US-20260129014-A1 - AI-DRIVEN GRAY ROUTE REDUCTION

US20260129014A1US 20260129014 A1US20260129014 A1US 20260129014A1US-20260129014-A1

Abstract

A method for detecting gray routed messages may include receiving a message from a sender via a network. The method may include determining one or more characteristics of the message. The method may include providing the one or more characteristics to a machine learning model, the machine learning model configured to assign a score to the message based at least in part on the one or more characteristics of the message. The method may include determining that the message is a gray-routed message based at least in part on the score assigned to the message. Based on a determination that the message is a gray-routed message, the method may include transmitting data indicating the message, the one or more characteristics of the message, and the sender to a contextual filtering system, the contextual filtering system configured to identify and filter gray-routed messages from the network.

Inventors

Mihir Bhatt
Jaynish PATEL

Assignees

Boost SubscriberCo L.L.C.

Dates

Publication Date: 20260507
Application Date: 20251219

Claims (20)

1 . A method for detecting gray routed messages, the method comprising: receiving, by a computing system, a message from a sender via a network; determining, by the computing system, geographical data associated with the messages, the geographical data indicating a route comprising a plurality of nodes and a location associated with each respective node; providing, by the computing system, the geographical data to a machine learning model; determining, by the machine learning model, that the geographical data is associated with gray-routed messages; generating, by the machine learning model, a score for the message based at least in part on the determination that the geographical data is associated with gray-routed messages; determining, by the computing system, that the message is a gray-routed message based at least in part on the score assigned to the message; and based on a determination that the message is a gray-routed message: transmitting, by the computing system, data indicating the message, the geographical data, and the sender to a contextual filtering system, the contextual filtering system configured to identify and filter gray-routed messages from the network.
2 . The method of claim 1 , wherein one or more characteristics comprise data associated with content of the message, the method further including: determining, by the machine learning model, that the content of the message includes indicators associated with illegitimate messages; and generating, by the machine learning model, the score for the message based at least in part on the indicators associated with the illegitimate messages and a comparison of the content of the message to a typical message.
3 . The method of claim 2 , wherein the machine learning model comprises a natural language processing model.
4 . The method of claim 1 , wherein the machine learning model comprises a geospatial data analysis model.
5 . The method of claim 1 , wherein one or more characteristics of the message comprises network information, the method further including: determining, by the machine learning model, that the network information of the message is atypical; and generating, by the machine learning model, the score for the message based at least in part on the determination that the network information of the message is atypical.
6 . The method of claim 5 , wherein the machine learning model comprises a clustering model.
7 . A system for detecting gray routed messages, comprising: one or more processors; a machine learning model; a non-transitory computer-readable medium comprising instructions that, when executed by the one or more processors, cause the system to perform operations to: receive, by a computing system, a message from a sender via a network; determine, by the computing system, geographical data associated with the messages, the geographical data indicating a route comprising a plurality of nodes and a location associated with each respective node; provide, by the computing system, the geographical data to a machine learning model; determine, by the machine learning model, that the geographical data is associated with gray-routed messages; generate, by the machine learning model, a score for the message based at least in part on the determination that the geographical data is associated with gray-routed messages; determine, by the computing system, that the message is a gray-routed message based at least in part on the score assigned to the message; and based on a determination that the message is a gray-routed message: transmit, by the computing system, data indicating the message, the geographical data, and the sender to a contextual filtering system, the contextual filtering system configured to identify and filter gray-routed messages from the network.
8 . The system of claim 7 , wherein the network is a cloud-based wireless network.
9 . The system of claim 7 , wherein the message is an application to person (A2P) message.
10 . The system of claim 7 , wherein the machine learning model comprises at least one of a clustering model, a sequential analysis model, and a natural language processing model.
11 . The system of claim 7 , wherein the instructions further cause the system to: receive data indicating an accuracy rating of a score assigned to the message; provide the message and the data indicating the accuracy rating of the score to the machine learning model such that the machine learning model is retrained based at least in part on the message and the accuracy rating of the score; and store the message and/or the data indicating the accuracy rating of the score in a historical dataset.
12 . The system of claim 7 , wherein the machine learning model comprises a rules-based filter, wherein rules of the rules-based filter are based at least in part on a regulation.
13 . The system of claim 7 , wherein the instructions further cause the system to perform operations to: determine a first routing plan associated with the network; provide the first routing plan to the machine learning model; determine, by the machine learning model, a predicted traffic window of the network, the predicted traffic window characterized by an increased network load; determine, by the machine learning model, a second routing plan such that messages are routed to prevent message congestion; and cause messages to be routed via the network according to the second routing plan.
14 . The system of claim 13 , wherein the machine learning model comprises a time series forecasting model.
15 . The system of claim 7 , wherein the contextual filtering system is associated with a 5G wireless network provider.
16 . A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving, by a computing system, a message from a sender via a network; determining, by the computing system, geographical data associated with the messages, the geographical data indicating a route comprising a plurality of nodes and a location associated with each respective node; providing, by the computing system, the geographical data to a machine learning model; determining, by the machine learning model, that the geographical data is associated with gray-routed messages; generating, by the machine learning model, a score for the message based at least in part on the determination that the geographical data is associated with gray-routed messages; determining, by the computing system, that the message is a gray-routed message based at least in part on the score assigned to the message; and based on a determination that the message is a gray-routed message: transmitting, by the computing system, data indicating the message, the geographical data, and the sender to a contextual filtering system, the contextual filtering system configured to identify and filter gray-routed messages from the network.
17 . The non-transitory computer-readable medium of claim 16 , wherein the network comprises an open-radio access network of a 5G wireless network provider.
18 . The non-transitory computer-readable medium of claim 16 , wherein at least one the plurality of nodes are associated with gray-routed messages.
19 . The non-transitory computer-readable medium of claim 16 , wherein the message is an application to person (A2P) message.
20 . The non-transitory computer-readable medium of claim 16 , wherein the machine learning model comprises a clustering model.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. Non-Provisional patent application Ser. No. 18/602,870, filed on Mar. 12, 2024, which is incorporated by reference for all purposes. BACKGROUND As organizations continue to utilize technology to reach individuals, bad actors continue to find new ways to abuse the same technologies. Application to person (A2P) messaging is one such technology. A bad actor may try to take advantage not only of the recipient of a message, but the network(s) involved in the messaging as well. The messages may be received via illegitimate routes called “gray-routes” that avoid may detection and pose issues in the collection of appropriate charges. BRIEF SUMMARY A method for detecting gray routed messages may include receiving, by a computing system, a message from a sender via a network. The method may include determining, by the computing system, one or more characteristics of the message. The method may include providing, by the computing system, the one or more characteristics to a machine learning model, the machine learning model configured to assign a score to the message based at least in part on the one or more characteristics of the message. The method may include determining, by the computing system, that the message is a gray-routed message based at least in part on the score assigned to the message. Based on a determination that the message is a gray-routed message, the method may include transmitting, by the computing system, data indicating the message, the one or more characteristics of the message, and the sender to a contextual filtering system, the contextual filtering system configured to identify and filter gray-routed messages from the network. In some embodiments, the one or more characteristics may include data associated with content of the message. The method may then include determining, by the machine learning module, that the content of the message includes indicators associated with illegitimate messages. The method may include generating, by the machine learning model, the score for the message based at least in part on the indicators associated with the illegitimate messages and a comparison of the content of the message to a typical message. The machine learning model may include a natural language processing model. In some embodiments, the one or more characteristics may include geographical data associated with the message. The method may then include determining, by the machine learning model, that the geographical data is associated with gray-routed messages. The method may include generating, by the machine learning model, the score for the message based at least in part on the determination that the geographical data is associated with gray routed messages. The machine learning model may include a geospatial data analysis model. In some embodiments, the one or more characteristics of the message may include network information. The method may then include determining, by the machine learning model, that the network information of the message is atypical. The method may include generating, by the machine learning model, the score for the message based at least in part on the determination that the network information of the message is atypical. The machine learning model may include a clustering model. A system for detecting gray routed messages may include one or more processors, a machine learning model, a contextual filtering system, and a non-transitory computer-readable medium. The non-transitory computer-readable medium may include instructions that, when executed by the one or more processors, cause the system to perform operations. According to the operations, the system may receive a message from a sender via a network. The system may determine one or more characteristics of the message. The system may provide the one or more characteristics to the machine learning model, the machine learning model configured to assign a score to the message based at least in part on the one or more characteristics of the message. The system may determine that the message is a gray-routed message based at least in part on the score assigned to the message. Based on a determination that the message is a gray-routed message, the system may transmit data indicating the message, the one or more characteristics of the message, and the sender to the contextual filtering system, the contextual filtering system configured to identify and filter illegitimate messages from the network. In some embodiments, the network may be a cloud-based wireless network. The message may be an application to person (A2P) message. The machine learning model may include at least one of a clustering model, a sequential analysis model, and a natural language processing model. In some embodiments, the instructions may further cause the system to receive data indicating an accuracy rating of the score assigned to the message. The system may provide t