Systems and methods for using machine learning for geographic analysis of access attempts
US12126646B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 12, 2022 |
| Grant date | Oct 22, 2024 |
| Priority date | — |
| Expiry date | Sep 28, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L2463/144
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
Disclosed herein are systems and methods for using machine learning for geographic analysis of access attempts. In an embodiment, a trained machine-learning model classifies source IP addresses of login attempts to a system as either blacklisted or allowed based on a set of aggregated features that correspond to login attempts to the system from the source IP addresses. The set of aggregated features includes, in association with each respective source IP address, a geographical login-attempt failure rate of login attempts to the system from each of one or more geographical areas that each correspond to the respective source IP address. Source IP addresses that are classified by the machine-learning model as blacklisted are added to a system blacklist, such that the system will disallow login attempts from such source IP addresses.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.