US 12,489,769 B2
System and method for identifying communications network anomalies of connected cars
Yaron Koral, Cherry Hill, NJ (US)
Assigned to AT&T Intellectual Property I, L.P., Atlanta, GA (US)
Filed by AT&T Intellectual Property I, L.P., Atlanta, GA (US)
Filed on Aug. 1, 2022, as Appl. No. 17/878,337.
Prior Publication US 2024/0039937 A1, Feb. 1, 2024
Int. Cl. H04L 67/12 (2022.01); H04L 9/40 (2022.01); H04L 41/06 (2022.01); H04L 41/16 (2022.01)
CPC H04L 63/1425 (2013.01) [H04L 41/06 (2013.01); H04L 41/16 (2013.01); H04L 63/1441 (2013.01); H04L 67/12 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A device, comprising:
a processing system including a processor; and
a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
gathering and aggregating historical data provided to an access point name (APN) gateway of a connected car manufacturer and a packet core of a communications network, wherein the historical data includes control plane data, and wherein the control plane data identifies authentication and network attack attempts, APN mismatches, and subscriber identity module (SIM) card mismatches;
using the historical data gathered and aggregated to train a machine-learning (ML) model to recognize anomalies from the historical data, thereby creating a trained ML model;
monitoring current data provided to the APN gateway and the packet core; and
generating, based on the monitoring, an alert when the trained ML model recognizes an anomaly in the current data, wherein the anomaly comprises signal quality of service issues, massive disconnects, short message service inconsistencies, location-related issues, and firmware version anomalies.