KR102213460B1 - System and method for generating software whistlist using machine run - Google Patents

Abstract

In a method for generating a software whitelist using machine learning performed by a user terminal according to an embodiment of the present invention, (a) a software file included in the software being stored in the user terminal is recognized, and attribute information is retrieved from the software file. Extracting; (b) calculating reliability information by performing a preset machine learning technique on the software file based on attribute information; (c) calculating a rating of software based on the reliability information, and generating a white list based on the rating; And (d) providing a white list to another user terminal or receiving a white list generated from another user terminal to update the white list, wherein the attribute information includes static data, dynamic data, heuristic data, and social engineering data. This is how to include.

Description

A system and method for creating a software white list using machine learning {SYSTEM AND METHOD FOR GENERATING SOFTWARE WHISTLIST USING MACHINE RUN}

The present invention relates to a system and method for generating a white list for authentication by recognizing it as safe software among software running in a terminal device.

In general, a method of registering and managing a white list is registered in a white list by a simple method such as a user's subjective judgment and digital signature. That is, the conventional whiteless registration method registers software that has been used in the past or those that have not been detected using an anti-virus product in the white list.

In particular, the conventional whitelist registration method is used from the user's point of view without objective indicators, or judged by reputation, or by checking the digital signature of an application software certification authority and inspecting it with an anti-virus product to determine whitelist registration only for those that are not abnormal. .

However, this method has high user dependence, low processing power, and requires expertise. In addition, new malicious software and variant malicious software that are not detected by anti-virus can be registered, and there are normal software that are not digitally signed, so it is difficult to objectify and automate registration in whitelist.

In general, when a blacklist-based malicious code detection method is used, the signature and reputation-based malicious code detection method has not already detected malicious software such as ransomware, and the behavior-based supplementing this is also detected due to diversification of patterns. It is going to be based on a white list, but it is difficult to register and manage the white list.

In addition, allowing simple whitelisting or blocking blacklisting may increase security threats due to whitelist registration of malicious software, or increase user discomfort due to blacklist registration of normal software.

The present invention is to solve the problems of the prior art described above, recognizes a file constituting software, collects feature and behavior information of the recognized software file, and extracts a pre-defined feature from the collected information, then machine learning Clustering for reliability is performed using. After that, the object is to implement a system that automatically registers, blocks, and deletes software in the whitelist for each grade based on reliability clustering.

However, the technical problem to be achieved by the present embodiment is not limited to the technical problem as described above, and other technical problems may exist.

As a technical means for achieving the above-described technical problem, in a method for generating a software white list using machine learning, performed by a user terminal according to an embodiment of the present invention, (a) the software being stored in the user terminal includes Recognizing a software file and extracting attribute information from the software file; (b) calculating reliability information by performing a preset machine learning technique on the software file based on attribute information; (c) calculating a rating of software based on the reliability information, and generating a white list based on the rating; And (d) providing a white list to another user terminal or receiving a white list generated from another user terminal to update the white list, wherein the attribute information includes static data, dynamic data, heuristic data, and social engineering data. It may be a method including.

In addition, the preset machine learning technique is applied by mixing deep learning CNN algorithm and machine learning word2vec algorithm for static data among attribute information, and supervised learning random forest algorithm and unsupervised learning for dynamic data, heuristic data and social engineering data. It may be a method of calculating a result value by applying a learning gan algorithm, and calculating reliability information by performing clustering of the result value through a k-mean algorithm.

In addition, the static data includes at least one of the presence or absence of a digital signature of the software to be verified, the presence of a file description, the latest version of the file, the latest version of the product, the existence of a product name, and the presence or absence of a copyright. The data includes at least one of process information calculated when the software to be verified is executed, memory status, installation path, execution and use history of the software to be verified, registration status, and execution status information of the software to be verified, and the heuristic data is subject to verification. Contains at least one of data on abnormal behavior including memory, capacity, CPU share, network, file input/output, thread usage, and threshold increase rate of the software, and social engineering data is executed at least once by the input device of the software to be verified It may be a method including at least one of whether or not, whether the file name, product name, and copyright information can be interpreted, whether the reference period of the execution time has elapsed, whether the usage rate is greater than or equal to the reference value, and whether or not user authentication is greater than or equal to the reference percentage.

In addition, it may be a method of dividing the grade by a preset number by clustering classified based on the reliability information, and matching with a higher grade as the reliability information is higher.

In addition, based on the rating, at least one of software execution, network connection, file i/o, child process creation, and folder access rights are applied differentially, but a higher rating may be a method in which more functions are applied. have.

In addition, backup data may be generated in an arbitrary storage area in the user terminal for the software file registered in the white list.

In addition, when a specific file included in a software file that has been registered in the white list is modified, a comparison value is calculated by comparing the backup data of the specific file with the modified specific file, and when the comparison value is less than a preset value, the white list When the software registered in is deleted and the comparison value is calculated to be greater than or equal to a preset value, the white list may be updated.

In addition, in the software removed from the white list, the white list may be updated by performing steps (a) to (b) again.

In addition, reliability information may be further provided to the whitelist sharing server, and reliability information generated by other user terminals may be received from the whitelist sharing server and applied to the machine learning technique.

In addition, a method of authenticating software after step (d), the method comprising: (e) detecting an executed software among software stored in a user terminal as whitelisted software to be verified; And (f) determining whether the software to be verified is registered on the white list, but if the software to be verified is not registered on the white list, steps (a) to (d) may be performed.

In addition, a user terminal for generating a software white list using machine learning, comprising: a memory storing a software white list generation program using machine learning; And a processor that executes a program stored in the memory, wherein the processor recognizes a software file included in the software stored in the user terminal, extracts attribute information from the software file, and a preset machine for the software file based on the attribute information Calculates reliability information by performing a learning technique, calculates a software grade based on the reliability information, creates a white list based on the grade, and provides a white list to another user terminal or a white list created from another user terminal Receives and updates the white list, and the attribute information may be a terminal including static data, dynamic data, heuristic data, and social engineering data.

In addition, it may be a computer-readable storage medium in which a program for performing a method for generating a software white list using machine learning is recorded.

The present invention recognizes a file constituting software, collects feature and behavior information of the recognized software file, extracts a predefined feature from the collected information, and performs clustering for reliability using machine learning. Thereafter, a system corresponding to automatic registration, blocking, and deletion of software in the whitelist for each grade can be implemented based on clustering for reliability.

1 is a diagram showing the configuration of a software white list generation system using machine learning according to an embodiment of the present invention.
2 is a diagram showing the configuration of a user terminal according to an embodiment of the present invention.
3 is an operation flow diagram illustrating a method of generating a software white list using machine learning according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and similar reference numerals are assigned to similar parts throughout the specification.

Throughout the specification, when a part is said to be "connected" to another part, this includes not only "directly connected" but also "electrically connected" with another element interposed therebetween. . In addition, when a part "includes" a certain component, it means that other components may be further included rather than excluding other components unless specifically stated to the contrary.

The following examples are detailed descriptions to aid understanding of the present invention, and do not limit the scope of the present invention. Accordingly, the invention of the same scope performing the same function as the present invention will also belong to the scope of the present invention.

1 is a diagram showing the configuration of a software white list generation system using machine learning according to an embodiment of the present invention.

Referring to FIG. 1, a software white list generation system 1 using machine learning is composed of a user terminal 100 and a server 200, and are interconnected through a communication network.

In this case, the user terminal 100 may refer to a terminal for generating a software white list using machine learning as a user terminal, and the server 200 may refer to a device for whitelist sharing.

According to an embodiment of the present invention, in the software white list generation system 1 using machine learning, the user terminal 100 recognizes a software file included in the software being stored, and extracts attribute information from the software file.

In addition, reliability information is calculated by performing a machine learning technique set in advance for a software file based on attribute information, and a software grade is determined based on the calculated reliability information.

Finally, a white list is created based on the rating, and the white list stored in the user terminal 100 is updated by sharing the white list with the server 200 or receiving the white list generated by another user terminal 100 To do.

In this case, the attribute report refers to information including at least one of static data, dynamic data, heuristic data, and social engineering data, and is described in detail as follows.

First, static data is information that includes at least one of whether the software to be verified has a digital signature, whether a file description is held, whether the file version is up-to-date, whether the product version is up-to-date, whether a product name is held, or whether a copyright is held. Can be

Secondly, the dynamic data is information including at least one of process information calculated when the software to be verified is executed, memory status, installation path, execution and use history of the software to be verified, registration status, and execution status information of the software to be verified. I can.

Thirdly, the heuristic data may be information including at least one of data on abnormal behavior including memory, capacity, CPU occupancy rate, network, file input/output, thread usage, and threshold increase rate of the software to be verified.

Fourth, social engineering data is determined whether or not the software to be verified is executed at least once by the input device, whether the file name, product name and copyright information can be interpreted, whether the execution time has elapsed, and whether the usage rate is greater than the reference value And it may be information including at least one of whether or not user authentication is greater than the reference percentage.

In addition, the level in the white list may be an index that determines whether or not a user can provide usage rights to a certain degree when using the software using the user terminal 100.

For example, based on the matching level of the software, at least one or more of the software execution, network connection, file i/o, child process creation, and folder access rights are differentially provided. Functions are not intended to limit the scope of the present invention as various functions may be applied in addition to the functions described herein.

At this time, if the rating matching the software is high, more functions can be duplicated and applied.

Conversely, the lower the rating, the more the application restricts the right to use the software, and when it is determined that the software is harmful to the user terminal 100, a function of blocking or deleting may be provided.

The user terminal 100 performs whitelist registration of software through a software whitelist generation application using machine learning.

The software whitelist generation application using machine learning may be an application embedded in the user terminal 100 or an application downloaded from an application distribution server and installed in the user terminal 100.

The plurality of user terminals 100 refer to communication terminals that can use a terminal application in a wired or wireless communication environment. Here, the user terminal 100 may be a user's portable terminal. In FIG. 1, the user terminal 100 is shown as a smart phone, which is a kind of portable terminal, but the spirit of the present invention is not limited thereto, and as described above, a terminal capable of mounting a terminal application is not limited. Can be borrowed.

To explain this in more detail, the user terminal 100 may include a handheld computing device (e.g., PDA, email client, etc.), any form of a mobile phone, or any form of another kind of computing or communication platform. However, the present invention is not limited thereto.

The server 200 is a server for sharing a white list, and a white list set through another user terminal 100 can be shared through the server 200.

Meanwhile, the communication network serves to connect the user terminal 100 and the server 200. That is, the communication network refers to a communication network that provides a connection path so that the user terminals 100 can transmit and receive data after accessing the server 200. Communication networks are wired networks such as LANs (Local Area Networks), WANs (Wide Area Networks), MANs (Metropolitan Area Networks), ISDNs (Integrated Service Digital Networks), and wireless networks such as wireless LANs, CDMA, Bluetooth, and satellite communications. However, the scope of the present invention is not limited thereto.

2 is a diagram showing the configuration of a user terminal according to an embodiment of the present invention.

Referring to FIG. 2, a user terminal 100 according to an embodiment of the present invention includes a communication module 110, a memory 120, and a processor 130.

In detail, the communication module 110 provides a communication interface necessary for providing a transmission/reception signal between the user terminal 100 and the server 200 in the form of packet data in connection with a communication network. Furthermore, the communication module 110 may receive a data request from the server 200 and transmit data as a response thereto.

Here, the communication module 110 may be a device including hardware and software necessary for transmitting and receiving a signal such as a control signal or a data signal through a wired or wireless connection with another network device.

In the memory 120, a program for performing a software white list generation method using machine learning is recorded. In addition, it performs a function of temporarily or permanently storing data processed by the processor 130. Here, the memory 120 may include a magnetic storage medium or a flash storage medium, but the scope of the present invention is not limited thereto.

In addition, information necessary for generating a white list including a white list may be stored in the memory 120. For example, a software file, attribute information extracted from the software file, and information on machine learning in which learning has been performed may be additionally stored.

In this case, detailed contents such as white list, software file, attribute information, etc. are the same as those described above with reference to FIG. 1.

The processor 130, as a kind of central processing unit, controls the entire process of generating a software white list using machine learning. Each step performed by the processor 130 will be described later with reference to FIG. 3.

Here, the processor 130 may include all types of devices capable of processing data, such as a processor. Here, the'processor' may refer to a data processing device embedded in hardware having a circuit physically structured to perform a function represented by a code or instruction included in a program. As an example of a data processing device built into the hardware as described above, a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, and an application-specific integrated (ASIC) circuit) and processing devices such as field programmable gate arrays (FPGAs), but the scope of the present invention is not limited thereto.

3 is an operation flow diagram illustrating a method of generating a software white list using machine learning according to an embodiment of the present invention.

Referring to FIG. 3, in order to generate a software white list using machine learning, the user terminal 100 recognizes a file of software and extracts attribute information (S310).

The application that generates a software white list using machine learning in step S310 recognizes a software file included in the software being stored in the memory 120 of the user terminal 100.

In this case, in an additional embodiment, the attribute information may be extracted by recognizing the software itself, not the file constituting the software.

Machine learning is learned through the attribute information extracted in step S310, and reliability information of the software file is calculated (S320).

In the attribute information extracted in step S310, a different algorithm is applied according to the type to calculate reliability information for the software file.

For example, static data is applied by mixing a deep learning CNN algorithm and a machine learning word2vec algorithm to calculate reliability information.

Reliability information is calculated by performing supervised learning random forest algorithm and unsupervised learning GAN for dynamic data, heuristic data and social engineering data.

In addition, for the result values for the reliability calculated through various algorithms for different attribute information, the reliability information is finally calculated by clustering the result values through the k-mean algorithm.

Based on the reliability information, the software grade is calculated and a white list is generated (S330).

The grade is divided by a preset number by clustering classified based on the reliability information calculated in step S320.

For example, if clustering is divided into 7 classifications, the grade is also divided into 7 grades.

In addition, the grade is divided according to the reliability information, but the higher the reliability information for the software file, the higher the grade is proportionally matched.

At this time, as described above, the authority given to each level varies. For example, level 1 is risk observation, level 2 is alert, level 3 is caution, level 4 is normal, level 5 is safety, level 6 is security, and level 7 is protection.

The white list generated in step S330 is provided to the server 200 and is shared with the other user terminal 100 (S340).

When the registration of the software in the white list is completed, information on the software file may be generated in a storage area of the user terminal 100. The information may be software's own information, usage records, and transformation logs.

If, after step S340, a specific file included in the registered software file in the generated white list is modified, the backup data of the specific file and the modified specific file are compared.

Thereafter, the comparison value is calculated, and it is checked whether or not it is out of the preset registration range of the white list. If the comparison value is calculated to be less than the preset value, the software is deleted from the white list. Conversely, if the comparison value is higher than the preset value, the white list is updated.

In this case, it is possible to update the white list by performing steps S310 to S340 again for software that has been modified to an optional embodiment and deleted from the white list.

The first white list generated based on the user terminal 100 may be provided to the server 200 and may be shared with other user terminals 100. Conversely, the second white list generated by the other user terminal 100 may be received from the server 200 and the first white list generated by the user terminal 100 may be updated. If necessary, the white list may be shared through communication between the user terminals 100 without going through the server 200.

In addition, as an additional embodiment, reliability information calculated through attribute information may be further provided to the whitelist sharing server 200. Through this, the user terminal 100 can receive the reliability information generated by the other user terminal 100 from the whitelist sharing server 200 and apply it to the machine learning technique.

Meanwhile, as an additional embodiment, the user terminal 100 compares the second white list received from the other user terminal 100 and the first white list previously stored in the user terminal 100 between the two. There may be cases with different grades. That is, the rating may be calculated differently for each user depending on the frequency or time of using the software. In this case, when updating the first white list, the software grade included in the first white list may be updated based on a higher grade among grade information of the same software registered in the first and second white lists. This is because it is possible to prevent a problem in which restrictions on use occur when the grade of software used by existing users is suddenly lowered.

In addition, as another additional embodiment, with reference to static data, dynamic data, heuristic data, and social engineering data of software mainly used in each user terminal 100, it is determined what kind of software is mainly used in each terminal. You can also detect the propensity for. For example, even with the same user terminal 100, in the case of a terminal commonly used in a company, a document-working software will be mainly used, so it can be concluded that it is a clerical propensity, whereas in the case of a terminal used in a general home, a game or video Since software that plays the game will be mainly used, it is possible to draw a conclusion that it is an entertainment tendency. Thereafter, whitelists generated by terminals having similar tendencies may be compared based on the user propensity information to update the whitelists in each terminal. In this case, even if the software level included in the whitelist of the user terminals 100 having similar tendencies is for the same software as the software level included in the whitelist of the user terminals 100 having different tendencies, the rating is defined differently. This is because it can happen.

After the step (S340) is finished, the application performs software authentication.

In this case, in the method of performing authentication, the executed software among software stored in the user terminal 100 is detected as the software to be verified in the white list.

After that, it is determined whether or not the software to be verified is registered on the white list, and the rating is lowered. If the software to be verified is not registered in the white list, steps S310 to S340 are performed to designate a level of the software and register it in the white list.

An embodiment of the present invention may also be implemented in the form of a recording medium including instructions executable by a computer, such as a program module executed by a computer. Computer-readable media can be any available media that can be accessed by a computer, and includes both volatile and nonvolatile media, removable and non-removable media. Further, the computer-readable medium may include a computer storage medium. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

Although the methods and systems of the present invention have been described in connection with specific embodiments, some or all of their components or operations may be implemented using a computer system having a general-purpose hardware architecture.

The above description of the present invention is for illustrative purposes only, and those of ordinary skill in the art to which the present invention pertains will be able to understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as being distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

1: Software whitelist generation system using machine learning
100: user terminal 200: server

Claims (12)

In a method for generating a software white list using machine learning performed by a user terminal,
(a) recognizing a software file included in the software being stored in the user terminal and extracting attribute information from the software file;
(b) calculating reliability information by performing a predetermined machine learning technique for the software file based on the attribute information;
(c) calculating a rating of the software based on the reliability information and generating a white list based on the rating; And
(d) providing the white list to another user terminal or receiving a white list generated from another user terminal to update the white list,
Step (d)
The reliability information is further provided to the whitelist sharing server,
Receiving the reliability information generated by another user terminal from the whitelist sharing server, applying it to the machine learning technique,
After step (d),
Based on the attribute information, a propensity for what kind of software is used in the user terminal is calculated, and the whitelists calculated by the user terminals corresponding to each other are compared, Update the whitelist,
The attribute information includes static data, dynamic data, heuristic data, and social engineering data. A method of generating a software white list using machine learning. The method of claim 1,
The preset machine learning technique,
Among the attribute information, a deep learning CNN algorithm and a machine learning word2vec algorithm are mixed and applied to the static data,
By applying supervised learning random forest algorithm and unsupervised learning gan algorithm to the dynamic data, heuristic data, and social engineering data, a result value is calculated,
The method for generating a software white list using machine learning to calculate the reliability information by performing clustering on the result value through a k-mean algorithm. The method of claim 1,
The static data,
It includes at least one of the presence or absence of a digital signature of the software subject to verification, the presence of a file description, the latest version of the file, the latest version of the product, the presence of a product name, and the presence of copyright, and
The dynamic data,
It includes at least one of process information calculated when the verification target software is executed, a memory state, an installation path, an execution and use history of the verification target software, a registration status, and execution state information of the verification target software,
The heuristic data,
At least one of data on abnormal behavior including memory, capacity, CPU share, network, file input/output, thread usage, and threshold increase rate of the verification target software,
The social engineering data,
Whether the software to be verified is executed at least once by the input device, whether the file name, product name, and copyright information can be interpreted, whether the execution time has elapsed, whether the usage rate is above the reference value, and user authentication of the reference percentage or more That includes at least one of whether or not, a method for generating a software white list using machine learning. The method of claim 1,
Dividing the grade by a preset number by clustering classified based on the reliability information,
Step (c)
The higher the reliability information, the higher the grade is matched. The method of claim 4,
Based on the rating, at least one of the software execution, network connection, file i/o, child process creation, and folder access rights are applied differentially,
If the rating is higher, more functions are applied. The method of claim 1,
Step (d)
To generate backup data in an arbitrary storage area in the user terminal for the software file registered in the white list is completed, a software white list generation method using machine learning. The method of claim 6,
When a specific file included in the software file registered in the white list is modified, a comparison value is calculated by comparing the backup data of the specific file with the modified specific file,
When the comparison value is calculated below a preset value, the software registered in the white list is deleted, and when the comparison value is calculated above a preset value, the white list is updated. Way. The method of claim 7,
In the software removed from the whitelist,
The method of generating a software white list using machine learning to update the white list by performing steps (a) to (b) again. delete The method of claim 1,
In the method of authenticating software after step (d),
(e) detecting an executed software among software stored in the user terminal as the software to be verified in the white list; And
(f) further comprising the step of determining whether the verification target software is registered in the white list,
If the verification target software is not registered in the white list, the (a) to (d) steps are performed, a software white list generation method using machine learning. In a user terminal generating a software white list using machine learning,
A memory in which a software whitelist generation program using machine learning is stored; And
Comprising a processor for executing a program stored in the memory,
The processor recognizes a software file included in the software stored in the user terminal, extracts attribute information from the software file, and calculates reliability information by performing a preset machine learning technique for the software file based on the attribute information. And, calculating the level of the software based on the reliability information, generating a white list based on the level, sharing the white list with a preset white list sharing server and receiving a white list generated by another user terminal , In the process of updating the white list and updating the white list, the reliability information is further provided to a white list sharing server, and the reliability information generated by another user terminal is received from the white list sharing server, and the machine After applying a learning technique and updating the white list, a propensity for what kind of software is used in the user terminal is calculated based on the attribute information, and the propensity for the user terminal corresponding to each other is calculated. By comparing the calculated white list, the white list of each user terminal is updated, and the attribute information includes static data, dynamic data, heuristic data and social engineering data, software white list using machine learning The user terminal to generate. A computer-readable storage medium in which a program for performing the method for generating a software white list using machine learning according to claim 1 is recorded.

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