US-12621299-B1 - Linguistic proficiency based protocols

US12621299B1US 12621299 B1US12621299 B1US 12621299B1US-12621299-B1

Abstract

A method for accessing a resource. A set of language affinities for multiple languages is identified in response to receiving a request from a client device for a user to access the resource. The user is authenticated using the set of language affinities for the multiple languages.

Inventors

Uri Kartoun
Sophie Batchelder
HAMID MAJDABADI
Jeremy R. Fox

Assignees

INTERNATIONAL BUSINESS MACHINES CORPORATION

Dates

Publication Date: 20260505
Application Date: 20241001

Claims (20)

1 . A method for accessing a resource, the method comprising: identifying a set of language affinities for multiple languages in response to receiving a request from a client device for a user to access the resource; generating a prompt using the multiple languages, wherein the prompt is a puzzle using the multiple languages, and wherein the puzzle comprises information encoded in a number of the multiple languages; determining an acceptable response to the prompt, wherein the acceptable response to the puzzle requires input in the number of the multiple languages; sending the prompt to the client device for the user; authenticating the user based on the set of language affinities for the multiple languages in response to receiving a response from the client device that matches the acceptable response; and authorizing access to the resource based on the authentication.
2 . The method of claim 1 , wherein the puzzle is a question using the multiple languages.
3 . The method of claim 1 , wherein generating the prompt comprises: generating the prompt using the multiple languages in which the prompt has a level of difficulty based on proficiency of the user.
4 . The method of claim 1 , wherein generating the prompt comprises: generating the prompt using the multiple languages in which the prompt has a level of difficulty based on a resource type for the resource.
5 . The method of claim 1 , wherein generating the prompt comprises: generating the prompt using a number of the multiple languages selected based on how many people speak the multiple languages in a geographic region.
6 . The method of claim 1 , wherein generating the prompt comprises: generating the prompt using a machine learning model system.
7 . The method of claim 1 further comprising: monitoring a digital engagement by the user; identifying language interaction samples from the digital engagement; and automatically determining a language proficiency for the user for the multiple languages using the language interaction samples for the user.
8 . The method of claim 1 , further comprising: determining a difficulty level for the puzzle based on: a security level associated with the resource being accessed; a number of languages to include in the puzzle; or a complexity of cross-linguistic relationships required to solve the puzzle and wherein generating the puzzle comprises generating the puzzle according to the determined difficulty level.
9 . The method of claim 1 , wherein identifying the set of language affinities further comprises: querying at least one of: a user profile database containing language proficiency data for the user; a geographic location database identifying geographic regions associated with the user; a communication history database storing prior communications in different languages by the user; or a social network database identifying language preferences indicated by the user.
10 . The method of claim 1 , wherein generating the puzzle comprises: selecting a first portion of information to be encoded in a first language of the multiple languages; selecting a second portion of information to be encoded in a second language of the multiple languages, wherein the second language is different from the first language; encoding the first portion and the second portion such that comprehension of both the first language and the second language is required to derive a correct solution to the puzzle; and wherein the acceptable response demonstrates understanding of information from both the first portion and the second portion.
11 . A computer system comprising: a processor set; a set of one or more computer-readable storage media; and program instructions, collectively stored in the set of one or more storage media to cause the processor set to perform operations comprising: identifying a set of language affinities for multiple languages in response to receiving a request from a client device for a user to access a resource; generating a prompt using the multiple languages, wherein the prompt is a puzzle using the multiple languages, and wherein the puzzle comprises information encoded in a number of the multiple languages; determining an acceptable response to the prompt, wherein the acceptable response to the puzzle requires input in the number of the multiple languages; sending the prompt to the client device for the user; authenticating the user based on the set of language affinities for the multiple languages in response to receiving a response from the client device that matches the acceptable response; and authorizing access to the resource based on the authentication.
12 . The computer system of claim 11 , wherein the puzzle is a question using the multiple languages.
13 . The computer system of claim 11 , wherein generating the prompt comprises: determining a proficiency of the user for the multiple languages in the set of language affinities; and generating the prompt using the multiple languages in which the prompt has a level of difficulty based on the proficiency of the user.
14 . The computer system of claim 11 , wherein generating the prompt comprises: generating the prompt using the multiple languages in which the prompt has a level of difficulty based on a resource type for the resource.
15 . The computer system of claim 11 , wherein generating the prompt comprises: generating the prompt using a number of the multiple languages selected based on how many people speak the multiple languages in a geographic region.
16 . The computer system of claim 11 , wherein generating the prompt comprises: generating the prompt using a machine learning model system.
17 . The computer system of claim 11 , further comprising: determining a difficulty level for the puzzle based on: a security level associated with the resource being accessed; a number of languages to include in the puzzle; or a complexity of cross-linguistic relationships required to solve the puzzle wherein generating the puzzle comprises generating the puzzle according to the determined difficulty level.
18 . The computer system of claim 11 , wherein identifying the set of language affinities further comprises: querying at least one of: a user profile database containing language proficiency data for the user; a geographic location database identifying geographic regions associated with the user; a communication history database storing prior communications in different languages by the user; or a social network database identifying language preferences indicated by the user.
19 . The computer system of claim 11 , wherein generating the puzzle further comprises: selecting a first portion of information to be encoded in a first language of the multiple languages; selecting a second portion of information to be encoded in a second language of the multiple languages, wherein the second language is different from the first language; encoding the first portion and the second portion such that comprehension of both the first language and the second language is required to derive a correct solution to the puzzle; and wherein the acceptable response demonstrates understanding of information from both the first portion and the second portion.
20 . A computer program product for accessing a resource, the computer program product comprising: a set of one or more computer-readable storage media; program instructions stored on the set of one or more storage media to perform operations comprising: identifying a set of language affinities for multiple languages in response to receiving a request from a client device for a user to access the resource; generating a prompt using the multiple languages, wherein the prompt is a puzzle using the multiple languages, and wherein the puzzle comprises information encoded in a number of the multiple languages; determining an acceptable response to the prompt, wherein the acceptable response to the puzzle requires input in the number of the multiple languages; sending the prompt to the client device for the user; authenticating the user based on the set of language affinities for the multiple languages in response to receiving a response from the client device that matches the acceptable response; and authorizing access to the resource based on the authentication.

Description

BACKGROUND The disclosure relates generally to an improved computer system and more specifically to security protocols using linguistic proficiencies Increasing reliance on digital platforms is occurring as well as the widespread adoption of online services. These platforms can include platforms that provide services such as banking, commercial transactions, healthcare portals, investment services, critical current exchanges, and other services. With these and other types of services, security mechanisms are important to prevent unauthorized access and cyber security attacks on the services and platforms on which the services are located. Security authentication methods can be used to prevent unauthorized access to services, reduce automated attacks, and reduce other undesired types of access. Two types of security authentication methods are two factor authentication (2FA) and Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). Two factor authentication is a security mechanism that enhances protection of accounts by requiring two forms of verification for granting access. This type of authentication involves an additional form of verification in addition to the standard education process to confirm the identity of the user. A first factor can be a user password. A second factor can be a token. This token can be, for example, a one-time password sent to a mobile device. Another example can be a biometric factor such as a fingerprint. As another example, the user may be asked to launch a specific app on the user's mobile phone and click on a link to approve the identity of the user. CAPTCHA is a security mechanism to verify that a user acting with a service is a human instead of an automated bot. This type of security mechanism involves a challenge response test that ensures the responses are received from a human user to prevent unauthorized automated access by an automated bot or other software application. CAPTCHA can be easy for humans to solve but difficult for automated bots to solve correctly. This challenge can include recognizing distorted text, identifying objects and images, or other types of challenges. SUMMARY According to one illustrative embodiment, a method for accessing a resource. A set of language affinities for multiple languages is identified in response to receiving a request from a client device for a user to access the resource. The user is authenticated using the set of language affinities for the multiple languages. According to other illustrative embodiments, a computer system and a computer program product for accessing a resource are provided. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram of a computing environment in accordance with an illustrative embodiment; FIG. 2 is a block diagram of an authentication environment in accordance with an illustrative embodiment; FIG. 3 is an illustration of a prompt in the form of a puzzle in accordance with an illustrative embodiment; FIG. 4 is an illustration of a prompt in accordance with an illustrative embodiment; FIG. 5 is an illustration of a prompt in accordance with an illustrative embodiment; FIG. 6 is an illustration of a prompt in accordance with an illustrative embodiment; FIG. 7 is a flowchart of a process for forming language-based authentication in accordance with an illustrative embodiment; FIG. 8 is a flowchart of a process for accessing a resource in accordance with an illustrative embodiment; FIG. 9 is a flowchart of a process for authenticating user in accordance with an illustrative embodiment; FIG. 10 is a flowchart of a process for generating a prompt in accordance with an illustrative embodiment; FIG. 11 is a flowchart of a process for generating a prompt in accordance with an illustrative embodiment; FIG. 12 is a flowchart of a process for generating a prompt in accordance with an illustrative embodiment; FIG. 13 is a flowchart of a process for generating a prompt in accordance with an illustrative embodiment; FIG. 14 is a flowchart of a process for determining a proficiency of the user in accordance with an illustrative embodiment; and FIG. 15 is a block diagram of a data processing system in accordance with an illustrative embodiment. DETAILED DESCRIPTION Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time. A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present d