KR101576993B1 - Method and System for preventing Login ID theft using captcha - Google Patents

Abstract

The present invention relates to a blocking method and a blocking system for ID stealing using a capcha. According to the present invention, since the timing of providing the capcha is variably provided according to the characteristics of the IP, it is possible to enhance the effect of blocking the possibility of the theft of the IP with high frequency of theft in advance.

Description

[0001] The present invention relates to a method and a system for preventing identity theft using a camera,

The present invention relates to a blocking method and a blocking system for ID stealing using a capcha.

A CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Aparts) provides a CAPTCHA problem to a user who accesses a web server and allows only the user who provided a response corresponding to the CAPTCHA problem to use the web server .

The cap car provides an issue that is difficult for an automated program to solve, thereby preventing an automated program from using the web server and allowing only the actual person to use the web server. An automated program can be a bot program or the like. In other words, the Capcha method is used to distinguish whether a respondent is a real human or a computer program through a test that can be easily solved by a person but can not be solved by current computer technology.

Such a CAPTCHA approach has played an important role as an effective solution to security problems on the Web. For example, if a user accesses a website to create an ID (subscription), the user is presented with a CAPTCHA test and only the user who has answered the correct answer for the presented test can generate the ID. Through this, it is possible to prevent the automatic generation of the ID by a malicious hacking program (Bot Program), thereby preventing the sending of spam and manipulation of the survey result.

KR 2012-0056748

In a preferred embodiment of the present invention, a user attempts to steal an identity by disabling an attempt to steal an ID, thereby protecting the user's personal information.

As a preferred embodiment of the present invention, in the Web service providing apparatus, the ID shoplifting method using the CAPTCHA is performed by using information obtained by accumulating the number of times that the login failure has occurred in the unit of one day in the past predetermined period, Generating a statistical value; Setting an adaptable threshold value that limits the number of log-in attempts by reflecting the daily log-on failure statistic value for each IP; IP, and providing a captcha when the number of failed login attempts exceeds the adaptation threshold value, and updating the adaptation threshold value in units of one day. In this case, the adaptation threshold value Are set differently for each IP.

Preferably, the step of setting an adaptive threshold value comprises: analyzing login log data accumulated over a predetermined period of time in the past by a logistic regression analysis method to determine whether a normal IP group in which there is no attempt to use a login ID, The unauthorized IP group having no previous use record, and then setting the adaptive threshold value for each of the normal IP group, the forgery IP group, and the unused IP group differently from each other .

Preferably, the adaptation threshold value indicates whether the IP is a domestic IP or an overseas IP by IP, the number of times of abuse for a predetermined period of time. A ratio of detection attempts during a predetermined period of time, a ratio of detection days during which the attempted theft attempt was detected in the past predetermined period, and a ratio of undetected days in which no attempted theft attempt has been detected in the past predetermined period And is further reflected and set.

As a preferred embodiment of the present invention, an ID shoplifting method using a CAPTCHA in an Internet portal site implemented in a terminal includes counting login failure counts by IP; Displaying a Capcha on the display if the number of login failures counted by IP exceeds the adaptation threshold; and updating the adaptation threshold in units of a day, wherein the adaptation threshold Values are set differently for each IP.

In another preferred embodiment of the present invention, an ID blocking system using a capcha includes an adaptive threshold value generation unit for generating an adaptive threshold value for each IP; A login manager for storing the number of login attempts by IP; A judging unit for judging whether the number of log-in attempts exceeds IP-allocated adaptation threshold value for each IP; And a cap carer for providing a cap car with an IP exceeding the adaptation threshold value, wherein the adaptation threshold value is a value that limits the number of login attempts per IP.

In a preferred embodiment of the present invention, the user's personal information is protected by disabling the attempt to steal the identity.

In a preferred embodiment of the present invention, there is an effect of preventing the ID from being stolen by enhancing the security of providing the CAPTCHA by setting the number of allowed login failures different by IP. In addition, by changing the number of allowed log-in failures in units of one day for each IP, it is possible to adaptively cope with a situation where problems such as theft and theft can occur.

FIG. 1 is a block diagram illustrating an internal configuration of an ID stealing interception system using a capcha according to an embodiment of the present invention.
Figs. 2 (a) to 2 (c) show the number of times of using the capcha in the normal ID and the ID of the stolen ID.
FIG. 3 illustrates a ratio of a captured car during a stealing attempt login detected as a preferred embodiment of the present invention.
4 (a) and 4 (b) illustrate an example of distinguishing a normal IP and a stolen IP using IP statistics and IP / 24 statistics according to a preferred embodiment of the present invention.
FIG. 5 shows a cumulative ratio of log-in failure ratios to log-in success ratios by interval in IP / 24 statistics, which is a preferred embodiment of the present invention.
FIG. 6 shows an example of classifying a stealth IP by grasping a log-on attempt according to IP classification and IP classification for an unused IP group having no history of use.
FIG. 7 illustrates an example of classifying log-in data by log-in ID in the ID-blocking system according to a preferred embodiment of the present invention.
FIG. 8 shows an example of calculating at least one or more information among the login success rate, the IP / 24 miss rate, the agent miss rate, and the cookie miss rate information by IP in the ID shoplifting system using the capcha .
FIG. 9 shows an example of an ID stealing interception system using a capsule, which is a preferred embodiment of the present invention.
FIG. 10 shows an example of a method for blocking an ID stealing using a capcha, which is a preferred embodiment of the present invention.
FIG. 11 is a block diagram illustrating an ID shading method using a CAPTCHA according to another embodiment of the present invention.
FIG. 12 is a flowchart illustrating a method for blocking an ID stealing in an ID stealing interception system using a cab of FIG. 9 according to an embodiment of the present invention.

FIG. 1 is a block diagram illustrating an internal configuration of an ID stealing interception system using a capcha according to an embodiment of the present invention.

The ID shading blocking system 100 using a CAPTCHA includes an adaptation threshold value generation unit 110, a log-in management unit 120, a determination unit 130, and a cap-

As a preferred embodiment of the present invention, the ID shovel blocking system 100 using a CAPTCHA can be set such that the threshold value for the number of unsuccessful login attempt attempts is different for each IP. In a preferred embodiment of the present invention this is referred to as an adaptation threshold.

Referring to FIGS. 2 (a) to 2 (c), it can be seen that the ratio of occurrence of the cap difference in the case of the stolen IP exceeds 90%.

The ID stealing interception system 100 using a CAPTCHA sets different adaptation threshold values for each IP and updates the set adaptation threshold values every day to set the adaptation threshold value to a low value for an IP with a high frequency of use, In the case of low frequency IP, the adaptation threshold value is set to be high.

Preferably, an IP having a high frequency of occurrence may be implemented by providing a CAPTCHA when two login failures occur, and a CAPTCHA when providing a login failure of six times with an IP having a low theft frequency.

To this end, the adaptive threshold generator 110 generates an adaptive threshold value for each IP in units of one day. In this case, the adaptation threshold value generation unit 110 analyzes log-log data accumulated during a predetermined period (for example, 90 days) using a logistic regression analysis method, and transmits the IP to the normal IP group in which no login- It can be classified into a fraudulent IP group in which an ID stealing attempt has occurred (see FIGS. 4 (a) and 4 (b)). In addition, it is also possible to set different adaptation threshold values for the normal IP group, the stolen IP group, and the unused IP group after classifying them into an unused IP group having no past record.

As a preferred embodiment of the present invention, the adaptive threshold generator 110 may set the adaptive threshold value of the normal IP group as follows.

* Adaptation threshold of normal IP group = MAX (15,0.8 * max (mean (login success ID) + 3 * stdev (login success ID), max (login success ID)))

In addition, the adaptation threshold value generator 110 may set the adaptation threshold value of the forged IP group as follows.

* Adaptation threshold of the stolen IP group = MAX (5,0.5 * max (mean (login success ID) + 3 * stdev (login success ID), max (login success ID)))

The adaptation threshold value generation unit 110 may generate at least one of the number of log-in attempts stored in the log management unit 120 for each IP, the number of successful log-in attempts, the number of unsuccessful log-in attempts, And can be additionally reflected when the adaptive threshold value is generated.

The login management unit 120 may store the number of log-in attempts by IP, and may store information about the number of log-in failures and the number of successful log-in attempts during log-in attempts.

The determination unit 130 is configured to previously store the IP-specific adaptive threshold table (see FIG. 9, S910) or the adaptive threshold value DB generated by the adaptive threshold generator 110 in advance. Thereafter, the login manager 120 receives and stores information about the number of unsuccessful login attempts by IP (refer to FIG. 9, S920).

The determination unit 130 determines whether the number of unsuccessful log-in attempt attempts by IP exceeds the pre-stored adaptive threshold value (see FIG. 9, S910). In the cap subtraction study 140, the determination unit 130 provides the capcha to the IP exceeding the adaptation threshold value.

Figs. 2 (a) to 2 (c) show the number of times of using the capcha in the normal ID and the ID of the stolen ID.

2 (a) shows a normal log-in attempt and a log-on log-in attempt among the IPs whose login failure count is five or more times. 4,146,175 out of all login IDs were detected as normal login attempts and 2,624,322 as fraudulent login attempts.

Fig. 2 (b) shows the number of times the cap difference is expected to appear in Fig. 2 (a), and Fig. 2 (c) shows the ratio of the cap sensor.

Referring to FIG. 2 (b), it is expected that 1,966 cap cars will be displayed in the normal login attempt, and 2,463,253 cap cars in the login attempt. Referring to FIG. 2 (c), the ratio of the capcha in the normal login attempt is 0.05%, while the ratio of the capcha in the stealing login attempt is 93.86%.

It can be seen from the above that the ratio of the capcha is considerably high in the forgery log-on attempt, and in the preferred embodiment of the present invention, .

FIG. 3 illustrates a ratio of a captured car during a stealing attempt login detected as a preferred embodiment of the present invention. Figure 3 shows an example of the actual rate at which a cap difference occurs during the attempted hijacking attempt detected during the period from November 2013 to February 2014.

4 (a) and 4 (b) illustrate an example of distinguishing a normal IP and a stolen IP using IP statistics and IP / 24 statistics according to a preferred embodiment of the present invention.

4 (a) and 4 (b), the x-axis represents the probability of the stealing IP at the time of analysis by the logistic regression analysis, and the y-axis represents the cumulative ratio of the normal IP.

Applying IP statistic and logistic regression analysis to IP / 24, the probability of stealing IP is low for normal IP 410, so the cumulative value is constant. However, since the stolen IP 420 has a high probability of being stolen, the cumulative ratio is kept low in the range of 0.1 to 0.8 where the stolen IP probability value is low, and the cumulative ratio is suddenly increased from 0.9 when the stolen IP probability is high can see.

In the graph of FIG. 5, the x-axis represents the ratio of "average login failure ID / daily average login success ID per day" at IP / 24, and the y-axis represents the cumulative ratio. Referring to the graph of FIG. 5, it is possible to know the ratio of the failed IDs to the IDs that successfully log in each day.

Referring to FIG. 5, as the ratio of "average daily log-in failure ID / daily average log-in success ID" per day in IP / 24 becomes larger than 0.3, the cumulative logon load ratio exceeds 50% Login success ID "ratio exceeds 0.35, it can be seen that the cumulative proportion of login attempts exceeds 80%.

FIG. 6 shows an example of classifying a stealth IP by grasping a log-on attempt according to IP classification and IP classification for an unused IP group having no history of use.

One embodiment of FIG. 6 shows that there is no IP / 24 record for a predetermined period of time (e.g., 70 days, November 25, 2013 to February 2, 2014) This is an example of the survey result.

In a preferred embodiment of the present invention, when setting an adaptation threshold value for an unused IP group having no past use record, the number of IPs used in Korea and the number of successful IDs are detected. Similarly, it detects the number of IPs used abroad and the number of IDs that succeeded in double login. After that, we determine the frequency of theft in Korea and overseas. The above process can be reflected in the adaptive threshold setting.

7 to 8 show an example for judging a stealing attempt. In the preferred embodiment of the present invention, the ID stealing blocking system is configured such that the IP / 16 information, the agent information and the cookie information of the login log data stored within 24 hours for each login ID, the login log data stored cumulatively for 90 days IP / 16 information, agent information, and cookie information of the login ID, and detects a matching login ID. Thereafter, the login IDs are reclassified for each IP as shown in FIG. 8, and the parameters for determining the abuse attempt are calculated.

FIG. 8 shows an example of calculating at least one or more information among the login success rate, the IP / 24 miss rate, the agent miss rate, and the cookie miss rate information by IP in the ID shoplifting system using the capcha .

8, at least one of the login success rate (S_rate), the IP / 16 miss rate (IP_miss), the agent miss rate (Agent_miss) and the cookie mistake rate (Cookie_miss) is calculated for each reclassified IP, Create a list of forged IP lists.

For example, if the IP is 3.3.1.3, the login success rate (S_rate) is very low at 10%, the IP / 16 miss rate (IP_miss) is 90%, the agent miss rate (Agent_miss) is 95%, and the cookie mistake rate (Cookie_miss) 90%. ≪ / RTI >

In this case, it can be estimated that the login ID is stolen if the predetermined condition is satisfied. For example, when the login success rate S_rate is 30% or less and the IP / 16 misfire rate IP_miss is 80% or more, the login success rate S_rate is 30% or less and the agent misissue Agent_miss is 70% A case where the login success rate (S_rate) is 20% or less and at the same time the cookie mistake rate (Cookie_miss) is 80% or more can be set as a specific condition.

When the web service provision server sets the stolen ID estimation condition to a case where the login success rate (S_rate) is 30% or less and the agent misissuation rate (Agent_miss) is 70% or more, 3.3.1.3 IP shown in FIG. 8 is estimated to be a stealing IP .

FIG. 9 shows an example of an ID stealing interception system using a capsule, which is a preferred embodiment of the present invention. FIG. 12 is a flowchart illustrating a method for blocking an ID stealing in an ID stealing interception system using a cab of FIG. 9 according to an embodiment of the present invention.

Referring to FIGS. 9 and 12, a method for blocking an ID stealing using a camcorder will be described as follows. Referring to FIG. 1, the adaptation threshold value generator (see FIG. 1, 110) of FIG. 1 is an IP Threshold maker server 910 of FIG. 9, and the login manager 120 of FIG. The deciding server 130 of FIG. 1 may be implemented as the Decision Server 930 of FIG. 1 and the Capping Server 140 of FIG. 1 may be implemented as a Captcha IP Server 940 of FIG. It is possible.

Referring to FIG. 9, description will be made below.

As a preferred embodiment of the present invention, the IP Threshold maker server 910 updates the adaptation threshold value for each IP every day (S910).

In addition, the IP Threshold maker server 910 classifies the IPs (S910) into a normal IP group in which no login ID hijacking attempt is made, a hijacked IP group in which the login ID hijacking attempt has occurred, The adaptive threshold value may be set differently for each of the IP group, the forgery IP group, and the unused IP group.

Thereafter, the IP Threshold maker server 910 transmits the IP-specific adaptation threshold value to the Decision Server 930, and the Decision Server 930 transmits the adaptation threshold value S920 from the IP Threshold maker server 910 every day And updates it.

When the user attempts to access the login server 920 by inputting the ID and the password, the login server 920 cumulatively transmits the number of login failures of the user to the Decision Server 930.

When the user's login attempt received from the login server 920 exceeds the adaptation threshold value S920 managed by the Decision Server 930, the Decision Server 930 notifies the Captcha IP Server 940 of this fact .

The Captcha IP Server 940 provides a CAPTCHA to the login server 920 when it is notified from the Decision Server 930 that the number of unsuccessful login attempts has exceeded the adaptation threshold value. The login server 920 prompts the user to input the CAPTCHA, the ID and the password.

FIG. 12 shows a flowchart of each server in FIG. 9 as a preferred embodiment of the present invention.

It should be noted that the configuration shown in FIG. 9 is only one embodiment for facilitating understanding of the present invention, and the configuration shown in FIG. 1 is not limited to the embodiment shown in FIG. 1 may be implemented by a plurality of devices as shown in FIG. 9, but it should be noted that the present invention can be implemented by a single integrated device.

12, the IP Threshold maker server 1230 generates an adaptive threshold value once a day and transmits the adaptive threshold value to the decision server 1240 (S1210). The decision server 1240 updates the adaptive threshold value every day.

The user inputs the ID and the password through the terminal 1210 used by the user (S1211). The login server 1120 confirms the login attempt of the user, and notifies the decision server 1140 of the login failure (S1230). Decision Server 1240 transmits a message to Captcha IP Server 1250 when the number of login failures received from Login Server 1220 exceeds the adaptation threshold value (S1220).

When the Captcha IP Server 1250 receives a message indicating that the number of failed login attempts exceeds the adaptation threshold value (S1220), the Captcha IP Server 1250 provides a capcha to the Login Server 1220 (S1221). The login server 1220 redirects the cabin login page to the user terminal 1210 (S1223).

As another embodiment of the present invention, the Login Server 1220 may implement a method of transmitting a message directly to the Captcha IP Server 1250 (S1250) and providing a Capcha for an IP providing a predetermined number of times or more (S1251)

FIG. 10 shows an example of a method for blocking an ID stealing using a capcha, which is a preferred embodiment of the present invention.

The ID shoplifting method using the capcha of the present invention can be implemented in a web service providing apparatus. The web service provision apparatus includes all terminals that provide Internet service through the Internet network. Examples of such terminals include a computer, a notebook, a mobile phone, a smart phone, a tablet PC, a handheld device, a thin terminal, and the like, and can be implemented as a multimedia device, an image processing device, a camcorder, and a camera. It also includes a communication unit and a terminal that implements the function of the input / output unit.

In an exemplary embodiment of the present invention, an Adaptable Threshold is set for each IP in order to block ID stealing using a CAPTCHA. To this end, in the web service providing apparatus, information that accumulates the number of times the login failure has occurred is used for a predetermined period (for example, 90 days) in units of one day.

In one embodiment, if the number of unsuccessful login failures has not exceeded three times on average in one day for 90 days in IP, the daily login failure statistics value is calculated to be 3 or less.

In another embodiment, if the number of unsuccessful login failures occurs one time over 90 days in IP 3.3.1.3 IP on average exceeds 5 times, the daily login failure statistics value is calculated to be 5 or more.

As described above, the past cumulative login failure value is reflected (S1010), and the daily login failure statistic value for each IP is generated (S1020). In this case, a logistic regression analysis method can be used.

Thereafter, an adaptive threshold value is set to limit the number of log-in attempts by reflecting the daily login failure statistics value for each IP. The adaptation threshold value is set to a lower value as the login failure statistic value increases.

For example, if the daily login failure statistics value of 1.1.2.1 IP is less than 3 and the daily login failure statistics value of 3.3.1.3 IP is more than 5, then 1.1.2.1 adaptation threshold of IP Is 4, and the login failure statistics value of 3.3.1.3 IP can be set to 2.

Also, as a preferred embodiment of the present invention, the adaptation threshold value may include whether the IP is a domestic IP or an overseas IP by IP, the number of times of theft attempt for a predetermined period of time. A ratio of detection attempts during a predetermined period of time, a ratio of detection days during which the attempted theft attempt was detected in the past predetermined period, and a ratio of undetected days in which no attempted theft attempt has been detected in the past predetermined period And can be further reflected and set.

Preferably, the adaptation threshold value is set by analyzing the login log data accumulated in the past predetermined period by a logistic regression analysis method, and the IP is classified into a normal IP group in which no attempt is made to steal the login ID, It can be divided into groups. In addition, it is possible to further classify the unused IP group having no past record by analyzing the log-log data accumulated during the predetermined period.

The adaptation threshold value can be set differently for each of the classified normal IP group, the forgery IP group, and the unused IP group. Preferably, the adaptive threshold value has an inversely proportional relationship with the number of times of theft attempt.

In the preferred embodiment of the present invention, the stealing attempt is performed by using IP / 16 information, agent information and cookie information of login log data stored within 24 hours for each login ID, IP / 16 information, agent information, and cookie information of the log data are compared with each other to detect whether there is log data that matches.

8, when the log-in ID of the log data detected is successful, the log-in success rate, the IP / 16 miss rate, the agent mis-rate, and the cookie mis- And calculating and detecting. In addition, when the theft attempt is detected, the frequency of providing the capsule may be used as shown in Figs. 2 (a) to (c) and Fig.

After setting the adaptation threshold value reflecting the login failure statistics in the past, the login failure count is counted for each IP having the login attempt (S1040). Thereafter, it is determined whether the number of log-in failures that occur for each IP exceeds the adaptation threshold value (S1050). If the number exceeds the adaptation threshold value (S1050), a capcha is provided (S1060).

As a preferred embodiment of the present invention, the web service provision apparatus may be configured to further store IP information providing the capcha. In addition, the adaptation threshold value can be updated by further reflecting the information providing the capcha when updating the adaptation threshold value. The higher the frequency of providing the capcha, the lower the adaptive threshold value can be set. In a preferred embodiment of the present invention, the adaptive threshold value is updated in units of one day (S1070), thereby blocking IDs continuously stolen.

FIG. 11 is a block diagram illustrating an ID shading method using a CAPTCHA according to another embodiment of the present invention. 11 is a flowchart illustrating a method of detecting identity theft using a CAPTCHA in an Internet portal site implemented in a terminal.

The terminal counts the number of log-in failures by the IP in which the log-in attempt occurred (S1110). If the number of log-in failures counted by IP exceeds the adaptation threshold value, a capcha is displayed on the display (S1120). In this case, the adaptive threshold value is updated in units of one day as described with reference to FIG. 10, and is set for each IP.

Also, the adaptation threshold value is whether the IP is a domestic IP or an overseas IP according to IP, the number of times of attempted abuse during a predetermined period. A ratio of detection attempts during a predetermined period of time, a ratio of detection days during which the attempted theft attempt was detected in the past predetermined period, and a ratio of undetected days in which no attempted theft attempt has been detected in the past predetermined period .

The method of the present invention can also be embodied as computer readable code on a computer readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored.

Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, and the like. The computer-readable recording medium may also be distributed over a networked computer system so that computer readable code can be stored and executed in a distributed manner.

The present invention has been described above with reference to preferred embodiments thereof. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Therefore, the disclosed embodiments should be considered in an illustrative rather than a restrictive sense. The scope of the present invention is defined by the appended claims rather than by the foregoing description, and all differences within the scope of equivalents thereof should be construed as being included in the present invention.

Claims (17)

A method for blocking an identity theft using a web service providing apparatus,
Generating a daily log-in failure statistic value for each IP by using information obtained by accumulating the number of log-in failures in one day for a predetermined period;
Setting an adaptable threshold value that limits the number of log-in attempts by reflecting the daily log-on failure statistic value for each IP;
Counting the number of unsuccessful logins for each IP and providing a captcha when the adaptation threshold value is exceeded;
And updating the adaptation threshold value in units of one day, wherein the adaptation threshold value is set for each IP. 2. The method of claim 1, wherein the step of setting an adaptive threshold comprises:
Log log data accumulated during the previous period are analyzed by logistic regression analysis,
A normal IP group in which the IP ID is not attempted to be stolen, a stolen IP group in which the login ID has been attempted to be stolen, and an unused IP group in which there is no history in use, and then the normal IP group, And an adaptation threshold value is set for each IP group. 2. The method of claim 1, wherein the adaptation threshold
And the adaptation threshold value is inversely related to the number of times of the theft attempt. 3. The method of claim 2,
Agent information and cookie information of login log data stored cumulatively for 90 days with IP / 16 information, agent information and cookie information of login log data stored within 24 hours by login ID, Is detected,
And when the corresponding log-in ID of the detected log data is successfully logged, the login success rate, the IP / 16 miss rate, the agent mis-rate, and the cookie mis-rate are calculated and detected for each IP. How to block identity theft using. 2. The method of claim 1, wherein the adaptation threshold
Whether the IP is a domestic IP or an overseas IP by IP, the number of times of attempted abuse for a predetermined period of time. A ratio of detection attempts during a predetermined period of time, a ratio of detection days during which the attempted theft attempt was detected in the past predetermined period, and a ratio of undetected days in which no attempted theft attempt has been detected in the past predetermined period Wherein the identification information of the ID card is set to be further reflected. The method according to claim 1,
Storing the IP information providing the capcha and further reflecting the stored information when setting the adaptation threshold value. The method according to claim 1,
Wherein the adaptive threshold value is lowered as the daily login failure statistic value becomes higher. 2. The method of claim 1, wherein the daily login failure statistics
Logistic regression analysis. ≪ RTI ID = 0.0 > 11. < / RTI > 2. The method of claim 1,
The method comprising the steps of: determining whether a user is allowed to hold personal information of an Internet portal company under a current law; 1. A method for detecting identity theft using a cep-
Counting the number of failed login attempts by IP;
Displaying a Capcha on the display if the number of login failures counted by IP exceeds the adaptation threshold; and
And updating the adaptation threshold value in units of one day,
Wherein the adaptation threshold value is set for each IP, and at least one or more times of the number of times of the login failures and the number of times of displaying the capscha on the display for each IP are accumulated in units of one day, and are reflected upon generation of the adaptation threshold value. Detection method using ID. 11. The method of claim 10, wherein the adaptation threshold
Whether the IP is a domestic IP or an overseas IP by IP, the number of times of attempted abuse for a predetermined period of time. A ratio of detection attempts during a predetermined period of time, a ratio of detection days during which the attempted theft attempt was detected in the past predetermined period, and a ratio of undetected days in which no attempted theft attempt has been detected in the past predetermined period Wherein the identification information is set to be further reflected. An adaptation threshold value generation unit for generating an adaptation threshold value for each IP;
A login manager for storing the number of login attempts by IP;
A judging unit for judging whether the number of log-in attempts exceeds IP-allocated adaptation threshold value for each IP; And
Wherein the adaptation threshold value is a value obtained by limiting the number of login attempts per IP, and the adaptation threshold value is a value obtained by limiting the number of login attempts , At least one or more of the number of successful login attempts at the login attempt, the number of unsuccessful login attempts at the login attempt, and the number of times the provided capcha is provided are reflected in the generation of the adaptive threshold value in units of one day. Theft blocking system. 13. The method of claim 12, wherein the adaptation threshold
And the IP address is updated every 1 day for each IP. delete 13. The apparatus of claim 12, wherein the adaptive threshold generator
Log log data accumulated during the previous period are analyzed by logistic regression analysis,
A normal IP group in which the IP ID is not attempted to be stolen, a stolen IP group in which the login ID has been attempted to be stolen, and an unused IP group in which there is no history in use, and then the normal IP group, Wherein the adaptive threshold value is set differently for each IP group. 16. The method of claim 15,
Agent information and cookie information of login log data stored cumulatively for 90 days with IP / 16 information, agent information and cookie information of login log data stored within 24 hours by login ID, Is detected,
And when the corresponding log-in ID of the detected log data is successfully logged, the login success rate, the IP / 16 miss rate, the agent mis-rate, and the cookie mis-rate are calculated and detected for each IP. ID blocking system used. 12. A computer program for executing the method according to any one of claims 1 to 11.

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