WO2021086170A1

WO2021086170A1 - Method and system to increase and improve authentication to access high profile data

Info

Publication number: WO2021086170A1
Application number: PCT/MY2020/050095
Authority: WO
Inventors: Suriani BINTI RAPA'EE; Sharipah BINTI SETAPA; Abd Aziz Arrashid BIN ABD RAJAK; Fazli BIN MAT NOR; Muhammad Hazwan BIN MOHD FOWZI
Original assignee: Mimos Berhad
Priority date: 2019-10-29
Filing date: 2020-09-29
Publication date: 2021-05-06

Abstract

The present invention relates to a computer implemented system (100) and method for providing a mutual re-verification in the event of at least one unsuccessful attempt during a traditional password authentication. The system (100) and method are provided with a captcha matching module (103) for matching each password with a captcha, a domain knowledge library creation module (104) for establishing a domain knowledge library (105) of captchas to map each captcha with a respective infographic based on a threshold value, an encapsulation module (106) for encapsulating the passwords, captchas, and infographics into first and second encapsulations, and a mutual re-verification module (107) for prompting the mutual re-verification on a third encapsulation by extending the traditional password authentication with a biometric verification upon an emerged captcha is matched with its respective infographic.

Description

METHOD AND SYSTEM TO INCREASE AND IMPROVE AUTHENTICATION TO ACCESS HIGH PROFILE DATA

FIELD OF INVENTION

The present invention relates to a field of information security and more particularly, to a verification method and system to improve authentication to access data.

BACKGROUND OF THE INVENTION

Passwords are the most commonly used authentication and verification process for a user to access their account via browser page or application. The password authentication requires users to create the password that only they know to access their accounts.

In certain situation the user may forget the password and therefore access to their account is denied. This situation is also known as a failed authentication. In this situation, a multiple try and error process is activated, this provides the user with an opportunity to change the password and/or recover the existing password. The multiple try and error process takes a lot of time to retrieve the existing password and gives the opportunity to compromise on the password. While the user performs the multiple try and error process it also gives the opportunity for a middle man or a hacker to change the password. Further, it gives the middle man the opportunity to corrupt the data and expose the data from the account. In order to overcome this problem, the verification programs provide a layer of security verification prior to accessing the account. One of the existing security verification is captcha which is a static verification and only depends on the user to acknowledge it. This causes the static verification (i.e. only depend on user) may still provide an opportunity for the middle man to access the account. Therefore, there is a need for high security verification programs or methods for the user authentication. There are several prior arts disclosing methods and systems for verification programs, some of which are listed below for reference. A United States Patent application number US20180239888A1 discloses a machine based verification method which includes the steps to acquire a plurality of objects. Next, generate an order of the plurality of objects and then generate a verification image that includes the plurality of objects and a prompt message that instruct the user to sequentially click on the plurality of objects in the verification image in the order of the plurality of objects.

Another cited prior art, the United States Patent application number US20180114225A1 discloses a voice verification method, comprises steps to obtain a user voice communication number at a network side, according to a voice verification request from a user side, and determine voice verification information corresponding to the voice verification request. Next, initiate a call to a corresponding user according to the user voice communication number at the network side, and in the case of call through, play the determined voice verification information to the user. Subsequently, perform an automatic listening and recording operation when the user enter into a call monitoring status, and upon termination of the call, send recording information obtained from the recording to the network side. Followed by determining a verification result according to the voice verification information and the recording information at the network side.

None of the above-cited prior arts disclose a method and system that generates a three stage encapsulation to provide mutual re-verification between the user and the system to access the data.

SUMMARY OF INVENTION

In a first aspect, the present invention provides a computer implemented method for providing a mutual re-verification in an event of at least one unsuccessful attempt during a traditional password authentication, the method comprising the steps of: matching, by a captcha matching module, each password with a captcha, whereby a user provides the password via an application module to access data; establishing, by a domain knowledge library creation module, a domain knowledge library of captchas to map each captcha with a respective infographic based on a threshold value; and encapsulating, by an encapsulation module, the passwords, captchas, and infographics into first and second encapsulations; wherein the method is characterised by the step of: prompting, by a mutual re-verification module, the mutual re-verifi cation on a third encapsulation by extending the traditional password authentication with a biometric verification, upon an emerged captcha is matched with its respective infographic.

Preferably, the first encapsulation is encapsulated by the password and captcha.

Preferably, the second encapsulation is encapsulated by the emerged captcha with its respective infographic.

Preferably, the method further comprises the step of providing, by the mutual re verification module, a selection for the user to choose another type of biometric verification in the event of failure of the mutual re-verification.

Preferably, the threshold value is determined by the steps of: applying an edit distance algorithm to obtain an edit distance value which represents the total amount of random strings that are quantified to be similar to the captcha; applying a pattern recognition algorithm to obtain infographics which are relevant to the captcha and their respective pattern recognition probabilistic value; and computing the threshold value for each infographic by dividing each pattern recognition probabilistic value over the edit distance value.

In a second aspect, the present invention provides a computer implemented system for providing a mutual re-verification in an event of at least one unsuccessful attempt during a traditional password authentication, the system comprises: a captcha matching module configured to match each password with a captcha, whereby a user provides the password via an application module to access data; a domain knowledge library creation module configured to establish a domain knowledge library of captchas to map each captcha with a respective infographic based on a threshold value; an encapsulation module configured to encapsulate the passwords, captchas, and infographics into first and second encapsulations; and characterised in that, the system further comprises: a mutual re-verification module configured to prompt the mutual re-verification on a third encapsulation by extending the traditional password authentication with a biometric verification upon an emerged captcha is matched with its respective infographic.

Preferably, the encapsulation module configured to perform the first encapsulation by encapsulating the password and captcha.

Preferably, the encapsulation module configured to perform the second encapsulation by encapsulating the emerged captcha with its respective infographic.

Preferably, the mutual re-verifi cation module is further configured to provide a selection for the user to choose another type of biometric verification in the event of failure of the mutual re-verifi cation.

Preferably, the domain knowledge library creation module is configured to apply edit distance algorithm to obtain an edit distance value which represents the total amount of random strings that are quantified to be similar to the captcha; apply pattern recognition algorithm to obtain infographics which are relevant to the captcha and their respective pattern recognition probabilistic value and compute the threshold value for each infographic by dividing each pattern recognition probabilistic value over the edit distance value.

BRIEF DESCRIPTION OF DRAWINGS These and other features, aspects, and advantages of the present invention will become better understood, when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

Figure. 1 is a block diagram illustrating a general architecture of a system to provide mutual re-verification in an event of at least one unsuccessful attempt during a traditional password authentication in accordance to an embodiment of the present invention.

Figure. 2 is a table illustrating relation and mapping between the traditional authentications with an alternative authentication.

Figure. 3 is a table illustrating an identification of infographics using the pattern recognition.

Figure. 4 is a table illustrating threshold points for the infographics.

Figure. 5 is a block diagram illustrating processing of non-conventional password with captcha and non-fix infographic in accordance to an embodiment of the present invention.

Figure. 6 is a table illustrating a flow sequence authentication prior to encapsulate the captcha and password into infographic in accordance to an embodiment of the present invention.

Figure. 7 is a block diagram illustrating an alternative mutual re-verification in accordance to an embodiment of the present invention.

Figure. 8 is a flow chart illustrating steps to provide mutual re-verification in the event of at least one unsuccessful attempt during a traditional password authentication in accordance to an embodiment of the present invention.

Figure. 9 is a flow chart illustrating steps to create a relationship between traditional authentication with an alternative authentication in accordance to an embodiment of the present invention.

Figure. 10 is a flow chart illustrating steps to determine a threshold probabilistic for the captcha in accordance to an embodiment of the present invention.

Figure. 11 is a flow chart illustrating steps to encapsulate the traditional authentication with captchas, and specific infographics in accordance to an embodiment of the present invention.

Figure. 12 is a flow chart illustrating steps to create a mutual re-verification to improve from existing static verification in accordance to an embodiment of the present invention.

Figure. 13 is a flow chart illustrating steps to establish mutual re-verifi cation between a user and the system in accordance to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The invention will now be described in greater detail, by way of example, with reference to the drawings.

The present invention provides a computer implemented method and system to provide a mutual re-verification in an event of an unsuccessful attempt during a traditional password authentication.

Referring to Figure 1, there is illustrated a general architecture of the system (100) to provide a mutual re-verifi cation in the event of at least one unsuccessful attempt during a traditional password authentication. The system (100) comprises an application module (101), a password database (102), a captcha matching module (103), a domain knowledge library creation module (104), a domain knowledge library (105), an encapsulation module (106) and a mutual re-verification module (107).

The application module (101) can be, but not limited to any kind of website, web browser or web application installed on a computing device. The application runs on a server and is in communication with the password database (102) via a communication network. The password database (102) preferably contains stored traditional passwords when users provide the password to log in to their account. If the entered password is correct, then the system (100) does not prompt any verification. In the case when the entered password is wrong, the system (100) prompts an alternative authentication and verification process instead of a multiple try and error process.

The captcha matching module (103) is preferably configured to match each password with a captcha. Following the matching step by the captcha matching module (103), the domain knowledge library creation module (104) is configured to establish the domain knowledge library (105) to store the captchas and to map each captcha with a respective infographic based on a threshold value. The encapsulation module (106) is then configured to encapsulate the passwords, captchas, and infographics into first and second encapsulations. Subsequently, the mutual re-verification module (107) is configured to prompt the mutual re-verification on a third encapsulation by extending the traditional password authentication with a biometric verification upon an emerged captcha is matched with its respective infographic.

Figure 2 describes the relation and mapping between the traditional password authentication with the alternative authentication. As indicated in Figure 2, the captcha matching module (103) is further configured to create domain criteria to support different criteria. Each password is clustered and profiled with its assigned captcha based on the created domain criteria. Preferably, the domain criteria includes but not limited to traditional password, alternative authentication, domain captcha and infographic. Then, the captcha is mapped with different non-fixed infographics and the criteria can be mapped with other criteria in order to select the specific infographic similar to the captcha for matching. Preferably, the domain knowledge library (105) of the captchas is established before the password is matched with the captcha. Next, the captcha is analysed in order to create a profile for the mutual re-verification. The created profile is then collected and a precise biometric mutual re-verification is generated based on the created profile. After that, a paradigm threshold is created by the captcha matching module (103) to support the mutual re-verification. Accordingly, a threshold value is assigned for each specific infographic as configured by the captcha matching module (103). Based on the threshold value, the captcha matching module (103) is then configured to obtain an accurate and precise infographic for the captcha as shown in Figure 2. The threshold value is preferably obtained using at least three different algorithms in order to improve the alternative authentication for the mutual re-verification. Specifically, the algorithms are edit distance, pattern recognition, and threshold value computation.

The domain knowledge library creation module (104) is preferably involved for applying the edit distance algorithm to determine a total amount of possible random strings (hereinafter referred to as ‘edit distance value’) which are almost similar to a respective captcha. The edit distance algorithm is a way of quantifying how dissimilar two strings (e.g., words) are to one another by counting the minimum number of operations required to transform one string into the other. Table 1 depicts an exemplary of random strings which are similar to a captcha example of “ice cream” using the edit distance algorithm. Preferably, the edit distance algorithm includes but not limited to substitution, insertion or deletion of an alphabet of the captcha. As shown in Table 1, the words “ice cream” is considered similar to “ice cream” as it is merely a deletion of the alphabet “a” and the edit distance value for this example is 7.

Table 1. List of suggested words similar to the captcha of “ice cream”.

Preferably, the domain knowledge library creation module (104) uses the pattern recognition algorithm to identify and classify the infographic based on the captcha as a pattern can either be viewed physically or it can be observed mathematically by applying algorithms. The pattern recognition can be defined as data classification based on knowledge already gained or on statistical information extracted from patterns and/or their representation. Most of the common pattern recognition algorithms known in the art are probabilistic in nature and statistical inference method is used to find the best infographic for the captcha. Figure 3 depicts different infographic examples for “ice cream” and their respective pattern recognition probabilistic value.

As shown in Figure 4, once the pattern recognition probabilistic values for the different infographics are obtained, the domain knowledge library creation module (104) further computes the threshold value in its probabilistic nature by dividing each pattern recognition probabilistic value over the edit distance value. The threshold value can be further presented in percentage by multiplying the threshold probabilistic value by one hundred. The encapsulation module (106) is configured to encapsulate the password with the captcha and the infographic in a sequence such that the encapsulation module (106) is configured to check if the sequence of the encapsulation is carried out in right order, as shown in Figure 6. The encapsulation module (106) is then configured to establish a correct mapping required for the encapsulation of the password into the captcha and the infographic. The encapsulation module (106) then checks for availability of the domain knowledge library (105) and updates the domain knowledge library (105) if required. Transformation with a biometric verification for further encapsulation is further extended by the encapsulation module (106) followed by selection expansion from the existing path to a new path by the mutual re-verifi cation module (107)) to include the mutual re-verification by the user and the system (100).

The mutual re-verification module (107) is configured to: check for a suitable infographic for the captcha; obtain a precise infographic for the captcha based on its threshold value; proceed with the mutual re-verification upon obtaining the precise infographic; expand the captcha and the infographic with a biometric verification as an alternative; obtain a matching biometric verification to align with the infographic and allows the user to acknowledge the infographic and the captcha via the biometric verification for the mutual re-verification; and check if the mutual re-verification is successful. In the event of failure of the mutual re-verification, the mutual re- verification module (107) is further configured to provide a selection for the user to select another type of biometric verification.

Figure 5 illustrates the block diagram illustrating the processing of non-conventional password with captcha and non-fix infographic carried out by the system (100). The user provides login details into the application module (101) to access data. Usually, if the attempt is unsuccessful due to error in input username or password, then user will be provided with another attempt. Prior to that, record of alternative authentication for respective user are stored in the domain knowledge library (105) and is matched to different captcha to protect from compromising on the password. If the captcha shown is matched and yield low threshold, then user can try again by utilizing alternative authentication. The password is first accessed, then the captcha is encapsulated. If the user is required to repeat the existing password, then the captcha emerges. If different captchas are emerged, then the user has to log out immediately once the user notices the captcha is wrong. However, this verification only depends on the user and does not include mutual verification.

Figure 6 illustrates the flow authentication prior to encapsulate the captcha and the password into the infographic. The encapsulation module (106) is configured to perform the first encapsulation is by encapsulating the password and the captcha. Then, the second encapsulation is performed by encapsulating the emerged captcha with similar infographic. Next, the third encapsulation is mutual re-verification by using biometric verification. For example, a voice can be used to re-verify the captcha emerged previously.

Figure 7 illustrates the alternative mutual re-verification carried out by the system (100). The alternative mutual re-verification is now explained with an example of the infographic which match with domain icefood as shown, for the captcha “ice cream”. Once the captcha and infographic are matched by the captcha matching module (103), the encapsulation module (106) is configured to perform the first, second and third encapsulation in the sequence as shown in the Figure 6. The mutual re-verifi cation module (107) is configured to perform the mutual re-verification process on third encapsulation using the biometric verification, for example using voice biometric such that the user has to read out the product name in the infographic and the captcha. This voice message can be acknowledged by both the system (100) and user dynamically, whereby the biometric verification is clicked by the user and the voice message is provided for acknowledgement. In the event whereby the previous mutual re-verification is failed (re-verifi cation is again activated to support previous failure), the mutual re-verification module (107) provides the selection for the user to select another type of biometric verification. The biometrics verifications can include voice, iris, finger, voice and any other biometrics known in the art. Referring now to Figure 8, a method for providing a mutual re-verification in the event of at least one unsuccessful attempt during a traditional password authentication is illustrated. Preferably, the user provides the traditional password into the application module (101) installed in the computing device. The application module (101) is in communication with a password database (102) to authenticate the password. In step 200, a relation is created between traditional authentication and the alternative authentication using the captcha matching module (103). Preferably, in this step each password is matched with the captcha using the captcha matching module (103). The matching of the password with the captcha is preferably performed based on criteria. In step 300, the threshold value is determined for the captcha in order to calculate the threshold value for the infographic using the domain knowledge library creation module (104). Preferably, this step further includes the step to establish the domain knowledge library (105) of the captchas to map each captcha with its respective infographic based on the threshold value, using the domain knowledge library creation module (104). In step 400, the passwords, captchas and infographics are encapsulated into first and second encapsulations, using the encapsulation module (106). Preferably, the password is encapsulated with another alternative authentication based on the availability of high profile data. This step further includes the step to extend the traditional authentication with a biometric verification upon an emerged captcha is matched with its respective infographic using a mutual re-verifi cation module (107). In steps 500 & 600, a mutual re-verification and establishment of the mutual re-verifi cation is created between the user and the system (100) using the mutual re-verification module (107).

Figure 9 illustrates the steps (201 to 208) for matching each traditional authentication with the alternative authentication which is carried out in sequence by the captcha matching module (103). In step 201, domain criteria is created. Preferably, in this step the domain criteria are created to support different criteria. For an example, different criteria are created to access high profile data report prior to alternative authentication. In step 202, the password and the captcha are clustered and profiled based on the domain criteria. In step 203, each criteria can communicate with each other to choose the specific infographic and prepare for mapping. The captcha is mapped with different non-fixed infographic. If the mapping is correct, then the user can proceed with another alternative authentication for mutual re-verification. Before the password is matched with the captcha, the domain knowledge library (105) of captcha is established and is mapped and transformed into suitable infographic based on the captcha. Each dynamic captcha selected is analyzed in order to create the profile for mutual re-verification. Each profile is then collected to generate a precise biometric mutual re-verification. In step 204, a mutual re-verification is selected for the clustered profile. In step 205, the domain knowledge library (105) is created. If this step fails to create the library (105) then it will revert back to step 201, else proceed to step 206. In step 206, create a new threshold value to support the mutual re-verification. In step 207, calculate a threshold range between the infographic and the mutual re-verification. In step 208, assign a threshold value to the specific infographic.

Referring to Figure 10, the steps 301 to 307 for determining the threshold value for the captcha and infographic is carried out in sequence, by the domain knowledge library creation module (104). In step 301, the captcha is generated, which is followed by step 302. In step 302, a plurality of random strings similar to the captcha is generated. In step 303, check for similarity that exists between the captcha and the random strings. If similarity between the captcha and the random strings is found, then proceed to step 304. If no similarity is found, it means that there may be a problem in the calculation and then the process further reverts back to step 301. In step 304, the similarity of two strings are quantified. In step 305, infographic gained based on the knowledge is classified. The infographics are identified preferably using the pattern recognition algorithm. In step 306, the threshold value in its probabilistic nature is determined and followed by step 307 that converts the threshold value from its probabilistic nature into percentage.

Referring to Figure 11, the steps 401 to 408 for encapsulating the passwords with the captchas, and specific infographics is carried out in sequence, by the encapsulation module (106). In step 401, the password is encapsulated with the captcha and the infographic in the sequence. Subsequently in step 402, the sequence of the encapsulation is checked if it is carried out in the right order as shown in Figure 6. Step 403 is prompted to establish the correct mapping required for the transformation of the password into the captcha and the infographic if the sequence of encapsulation is carried out in the right order. If the sequence of the encapsulation is not in the right order, step 406 is initiated to check availability of the domain knowledge library (105) and step 407 is provoked to update the domain knowledge library (105) if required. In step 404, the encapsulation is extended with the biometric verification for the further encapsulation. In step 405, a selection is expanded from an existing path to support the mutual re-verification. Preferably, the new path is eligible after the second encapsulation.

Referring to Figure 12, the steps 501 to 506 for creating the mutual re-verification to improve from existing static verification is carried out in sequence, by the mutual re- verification module (107). In step 501, suitable infographic is checked for the captcha, whereby the precise infographic for the captcha based on its threshold value is obtained in step 502. Mutual re-verification is proceeded in step 503 upon obtaining the precise infographic, in which the mutual re-verification is selected in step 504. In step 505, if the mutual re-verification is selected, then step 506 is prompted to establish negotiation. If the mutual re-verification is not selected, step 501 re-initiates and the following steps are repeated until the mutual re-verification is selected.

Referring to Figure 13, the steps 601 to 606 for establishing the mutual re-verification between the user and the system (100) is carried out in sequence, by the mutual re- verification module (107). In step 601, the captcha and the infographic is expanded to the mutual re-verification, whereby the mutual re-verification is expanded into biometric verification as the alternative as seen in step 602. The matching biometric verification is then obtained to align with the infographic in step 603. Following, the user then clicks to acknowledge or agrees with the selected biometric verification by reading out the product name in the infographic and the captcha. In step 605, the mutual re-verification is checked whether it is successful. If the mutual re-verification is not successful, then step 607 initiates to allow the user to select another type of biometric verification. Both the system (100) and user can negotiate for the mutual re verification. If the mutual re-verifi cation is successful in the step 605, then step 606 is proceeded with mutual acceptance by the user and the system (100) and the process is ended.

The present disclosure includes as contained in the appended claims, as well as that of the foregoing description. Although this invention has been described in its preferred form with a degree of particularity, it is understood that the present disclosure of the preferred form has been made only by way of example and that numerous changes in the details of construction and the combination and arrangements of parts may be resorted to without departing from the scope of the invention.

Claims

1. A computer implemented method for providing a mutual re-verification in an event of at least one unsuccessful attempt during a traditional password authentication, the method comprising the steps of: matching, by a captcha matching module (103), each password with a captcha, whereby a user provides the password via an application module (101) to access data; establishing, by a domain knowledge library creation module (104), a domain knowledge library (105) of captchas to map each captcha with a respective infographic based on a threshold value; and encapsulating, by an encapsulation module (106), the passwords, captchas, and infographics into first and second encapsulations, wherein the method is characterised by the step of: prompting, by a mutual re-verifi cation module (107), the mutual re- verification on a third encapsulation by extending the traditional password authentication with a biometric verification, upon an emerged captcha is matched with its respective infographic.

2. The method according to claim 1, wherein the first encapsulation is encapsulated by the password and captcha.

3. The method according to claim 1, wherein the second encapsulation is encapsulated by the emerged captcha with its respective infographic.

4. The method according to claim 1, further comprising the step of providing, by the mutual re-verification module (107), a selection for the user to choose another type of biometric verification in an event of failure of the mutual re- verifi cation.

5. The method according to claim 1, wherein the threshold value is determined by the steps of: applying an edit distance algorithm to obtain an edit distance value which represents the total amount of random strings that are quantified to be similar to the captcha; applying a pattern recognition algorithm to obtain infographics which are relevant to the captcha and their respective pattern recognition probabilistic value; and computing the threshold value for each infographic by dividing each pattern recognition probabilistic value over the edit distance value.

6. A computer implemented system (100) for providing a mutual re-verification in an event of at least one unsuccessful attempt during a traditional password authentication, the system (100) comprising: a captcha matching module (103) configured to match each password with a captcha, whereby a user provides the password via an application module (101) to access data; a domain knowledge library creation module (104) configured to establish a domain knowledge library (105) of captchas to map each captcha with a respective infographic based on a threshold value; and an encapsulation module (106) configured to encapsulate the passwords, captchas, and infographics into first and second encapsulations, characterised in that, the system (100) further comprises: a mutual re-verification module (107) configured to prompt the mutual re-verifi cation on a third encapsulation by extending the traditional password authentication with a biometric verification upon an emerged captcha is matched with its respective infographic.

7. The system (100) according to claim 6, wherein the encapsulation module (106) is configured to perform the first encapsulation by encapsulating the password and captcha.

8. The system (100) according to claim 6, wherein the encapsulation module (106) is configured to perform the second encapsulation by encapsulating the emerged captcha with its respective infographic.

9. The system (100) according to claim 6, wherein the mutual re-verifi cation module (107) is further configured to provide a selection for the user to choose another type of biometric verification in an event of failure of the mutual re- verifi cation.

10. The system (100) according to claim 6, wherein the domain knowledge library creation module (104) is configured to apply edit distance algorithm to obtain an edit distance value which represents the total amount of random strings that are quantified to be similar to the captcha; apply pattern recognition algorithm to obtain infographics which are relevant to the captcha and their respective pattern recognition probabilistic value and compute the threshold value for each infographic by dividing each pattern recognition probabilistic value over the edit distance value.