WO2020113401A1 - Procédé, appareil et dispositif de détection de données - Google Patents

Procédé, appareil et dispositif de détection de données Download PDF

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Publication number
WO2020113401A1
WO2020113401A1 PCT/CN2018/119060 CN2018119060W WO2020113401A1 WO 2020113401 A1 WO2020113401 A1 WO 2020113401A1 CN 2018119060 W CN2018119060 W CN 2018119060W WO 2020113401 A1 WO2020113401 A1 WO 2020113401A1
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Prior art keywords
data
detected
attribute information
security
preset
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Application number
PCT/CN2018/119060
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English (en)
Chinese (zh)
Inventor
刘奎龙
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北京比特大陆科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 北京比特大陆科技有限公司 filed Critical 北京比特大陆科技有限公司
Priority to PCT/CN2018/119060 priority Critical patent/WO2020113401A1/fr
Priority to CN201880098312.7A priority patent/CN113316921A/zh
Publication of WO2020113401A1 publication Critical patent/WO2020113401A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/40—Network security protocols

Definitions

  • This application relates to the field of computer technology, for example, to a data detection method, device, and equipment.
  • the embodiments of the present disclosure provide a data detection method, device and equipment, which improve the efficiency and accuracy of data detection.
  • an embodiment of the present disclosure provides a data detection method, including:
  • the security of the data to be detected is detected, and the preset model is obtained by learning the sample data through a neural network.
  • the method further includes:
  • the attribute information of the data to be detected includes at least one of the following attributes: a format of the data to be detected, resources used by the data to be detected, and a frequency of data transmission.
  • the data transmission frequency is a frequency at which the device that sends the data to be detected sends data within a preset time period.
  • the detecting the security of the data to be detected according to the attribute information of the data to be detected and the preset model includes:
  • the data to be detected is safety data according to the attribute information
  • the method further includes:
  • the detecting the security of the data to be detected according to the attribute information includes:
  • the security range corresponding to the format of the data to be detected includes at least one preset security format.
  • the security range corresponding to the resource used by the data to be detected includes a preset resource range.
  • the safety range corresponding to the data transmission frequency includes a preset frequency range.
  • the performing security verification on the attribute information and/or the data to be detected through the preset model includes:
  • the type of the data to be detected includes at least one of a text type, an image type, a voice type, or a video type;
  • the sample data includes marked safety sample data and marked risk sample data.
  • an embodiment of the present disclosure provides a data detection device, including a detection module
  • the detection module is configured to detect the security of the data to be detected according to the attribute information of the data to be detected and a preset model, and the preset model is obtained by learning the sample data through a neural network.
  • the device further includes a first acquisition module
  • the first acquiring module is configured to acquire attribute information of the data to be detected.
  • the attribute information of the data to be detected includes at least one of the following attributes: a format of the data to be detected, a resource used by the data to be detected, and a frequency of data transmission.
  • the data transmission frequency is a frequency at which the device that sends the data to be detected sends data within a preset time period.
  • the detection module is configured to:
  • the data to be detected is safety data according to the attribute information
  • the device further includes a determination module, and the determination module is configured to:
  • the detection module detects that the data to be detected is risk data according to the attribute information, it is determined that the data to be detected is risk data.
  • the detection module is configured to:
  • the security range corresponding to the format of the data to be detected includes at least one preset security format.
  • the security range corresponding to the resource used by the data to be detected includes a preset resource range.
  • the safety range corresponding to the data transmission frequency includes a preset frequency range.
  • the device further includes a second acquisition module and a third acquisition module,
  • the second acquiring module is configured to acquire the type of the data to be detected, and the type of the data to be detected includes at least one of a text type, an image type, a voice type, or a video type;
  • the third obtaining module is configured to obtain the preset model according to the type of the data to be detected
  • the detection module is configured to perform security verification on the attribute information and/or the data to be detected through the preset model.
  • the sample data includes marked safety sample data and marked risk sample data.
  • an embodiment of the present disclosure provides a computer including the device according to any one of the second aspect.
  • an electronic device including:
  • At least one processor At least one processor
  • a memory communicatively connected to the at least one processor; wherein,
  • the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, causes the at least one processor to perform the method of any one of the first aspects.
  • an embodiment of the present disclosure provides a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are configured to perform the method according to any one of the first aspects.
  • an embodiment of the present disclosure provides a computer program product, characterized in that the computer program product includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions, when the program instructions When executed by a computer, the computer is caused to perform the method of any one of the first aspects.
  • the data detection method, device and equipment provided by the embodiments of the present disclosure can detect the security of the data to be detected according to the attribute information of the data to be detected and the preset model, because the preset model is obtained by learning the sample data through the neural network
  • the sample data includes marked safety sample data and marked risk sample data. Therefore, according to the preset model, you can accurately verify whether the data to be tested is safe. In the above process, not only save labor costs, but also improve detection efficiency and improve The accuracy of detection.
  • FIG. 1 is an architectural diagram of a data monitoring method provided by an embodiment of the present disclosure
  • FIG. 2 is a schematic flowchart of a data detection method according to an embodiment of the present disclosure
  • FIG. 3 is a schematic flowchart of a method for detecting data to be detected based on attribute information according to an embodiment of the present disclosure
  • FIG. 4 is a schematic flowchart of another data detection method provided by an embodiment of the present disclosure.
  • FIG. 5 is a schematic structural diagram of a data detection device according to an embodiment of the present disclosure.
  • FIG. 6 is a schematic structural diagram of another data detection device according to an embodiment of the present disclosure.
  • FIG. 7 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.
  • the scenarios used by the data detection method shown in this application will be exemplified.
  • the important interface in the network for example, the network gateway from the internal network to the external network
  • the important interface in the network detects the data passing through the important interface to determine the data Whether it is confidential data. If the data is confidential data, the data is prohibited from being sent through the important interface. In this way, leakage of important data can be avoided.
  • the second possible application scenario after receiving the data, before processing the data, you can first verify whether the data is attack data, and when the data is determined to be attack data, discard the data, so that you can avoid being affected. Network attacks.
  • the above describes the application scenarios that can be used in the present application in the form of examples, and is not a limitation of the application scenarios. In the actual application process, the application scenarios can be determined according to actual needs, which is not limited in this application.
  • FIG. 1 is an architectural diagram of a data monitoring method provided by an embodiment of the present disclosure.
  • the attribute information of the data to be detected can be obtained first, and whether the data to be detected is safe is verified according to the attribute information of the data to be detected first.
  • risk data unsafe data
  • the security information of the attribute information of the data to be tested and/or the data to be tested is verified through the preset model, and the data to be tested is verified through the preset model verification If it is risk data, it is determined that the data to be tested is risk data, and after the preset model is verified to detect that the data to be tested is safe data, then the data to be tested is determined to be safe data.
  • the attribute information of the data to be detected is firstly subjected to a rough security verification of the data to be detected.
  • the verification process is simple and convenient.
  • the data to be detected is determined to be safe data according to the attribute information
  • the Set the model to verify the security of the data to be tested Since the preset model is obtained by learning the sample data through the neural network, the sample data includes the marked safety sample data and the marked risk sample data. Therefore, according to the preset model, it can accurately verify whether the data to be tested is safe. In the above process In addition to saving labor costs, it can also improve detection efficiency and accuracy.
  • FIG. 2 is a schematic flowchart of a data detection method according to an embodiment of the present disclosure. Please refer to FIG. 2, the method may include:
  • the execution subject of the embodiments of the present disclosure may be an electronic device or a data detection device provided in the electronic device.
  • the data detection device may be implemented by software or a combination of software and hardware.
  • the electronic device may be user equipment, for example, mobile phones, computers, and other devices.
  • the electronic device may also be a device such as a gateway or a server.
  • the data to be detected may be any data transmitted in the network.
  • the attribute information of the data to be detected includes at least one of the following attributes: the format of the data to be detected, the resources used by the data to be detected, and the frequency of data transmission.
  • the format of the data to be detected may include doc format, PPT format, HTML format, JPG format, and so on.
  • the format of the data to be detected may also include other, which is not limited in the embodiments of the present disclosure.
  • the resources used by the data to be detected may include network resources, CPU resources, memory resources, and so on.
  • the network resource may be network traffic, network bandwidth, and so on.
  • the data sending frequency may be the frequency of sending data by the device sending the data to be detected.
  • the data sending frequency is the frequency at which the device sending the data to be detected sends data within a preset time period.
  • the frequency of data transmission may be the number of data transmissions per unit time.
  • the unit time may be 1 hour, 1 minute, and so on.
  • the preset time period may be a time period of a preset duration before the current time.
  • the preset duration may be 1 hour, 3 hours, one day, etc.
  • the preset duration can be set according to actual needs.
  • S202 Detect the security of the data to be detected according to the attribute information of the data to be detected and the preset model.
  • the preset model is obtained by learning the sample data through a neural network.
  • the neural network may be a deep neural network.
  • the sample data may include marked safety sample data and marked risk sample data.
  • the marked sample data refers to the sample data determined as safety data.
  • the marked risk sample data refers to the sample data determined as risk data.
  • the security of the data to be detected may be detected according to the attribute information of the data to be detected first.
  • the data to be detected is risk data according to the attribute information of the data to be detected
  • it may be determined that the data to be detected is risk data.
  • security verification may be performed on the attribute information of the data to be detected and/or the data to be detected through a preset model.
  • the data to be detected When detecting that the data to be detected is safe data based on the attribute information of the data to be detected and/or the data to be detected through the preset model, it can be determined that the data to be detected is safe data; /Or when the data to be detected detects that the data to be detected is risk data, it may be determined that the data to be detected is risk data.
  • the security of the data to be detected can be detected based on the attribute information in the following feasible implementation manners:
  • the security range corresponding to the format of the data to be detected includes at least one preset security format.
  • the preset security format included in the security range may be preset.
  • the security range corresponding to the resource used by the data to be detected includes a preset resource range.
  • the preset resource range may include a CPU occupancy range, a memory occupancy range, and a traffic occupancy range.
  • the safety range corresponding to the data transmission frequency includes a preset frequency range.
  • the preset frequency range includes the maximum frequency and the minimum frequency.
  • the type of the data to be detected may be obtained, and a preset model may be obtained according to the type of the data to be detected; the preset model may be used to perform security verification on the attribute information and/or the data to be detected.
  • the type of data to be detected includes at least one of a text type, an image type, a voice type, or a video type.
  • a preset model corresponding to a data type can perform security verification on the data of the data type.
  • the preset models corresponding to different data types are learned according to the sample data corresponding to the data types.
  • the preset model corresponding to the text type is obtained by learning based on the sample data of the text type.
  • the text-type sample data includes text-type sample data marked as safety data, and text-type sample data marked as risk data.
  • the preset model corresponding to the image type is learned based on the sample data of the image type.
  • the sample data of the image type includes sample data marked as safety data of the text type and sample data marked as risk data of the image type.
  • the data detection method provided by the embodiment of the present disclosure can detect the security of the data to be detected according to the attribute information of the data to be detected and the preset model. Since the preset model is obtained by learning the sample data through the neural network, the sample data includes Marked safety sample data and marked risk sample data, therefore, it can accurately verify whether the data to be tested is safe according to the preset model. In the above process, not only save labor costs, but also improve the detection efficiency and improve the accuracy of detection .
  • FIG. 3 is a schematic flowchart of a method for detecting data to be detected according to attribute information according to an embodiment of the present disclosure. Please refer to FIG. 3, the method may include:
  • the security range corresponding to the format of the data to be detected is acquired, and the security range corresponding to the format of the data to be detected includes at least one preset security format.
  • the preset security format may include doc format, HTML format, and so on.
  • the security range corresponding to the resource used by the data to be detected is acquired, and the security range corresponding to the resource used by the data to be detected includes the preset resource range.
  • the preset resource range may include 1%-30% of CPU occupancy.
  • the preset resource range may include 1%-50% of memory resource occupancy.
  • the preset resource range may include traffic resource occupancy: 1M/s-300M/s.
  • the safety range corresponding to the data transmission frequency is acquired, and the safety range corresponding to the data transmission frequency includes the preset frequency range.
  • the preset frequency range can be 1 time/hour-50 times/decimal.
  • the risk data may be confidential data.
  • the risk data may be attack data.
  • the security data may be non-confidential data.
  • the security data may be non-attack data.
  • the risk range corresponding to each attribute may also be set in advance, and it is determined whether each attribute in the attribute information is within the corresponding risk range.
  • the data to be detected is risk data.
  • the data to be detected is safe data.
  • the attribute information corresponding to the data to be detected and the security range corresponding to each attribute in the attribute information can quickly determine whether the data to be detected is safe data or risk data.
  • FIG. 4 is a schematic flowchart of another data detection method provided by an embodiment of the present disclosure. Please refer to FIG. 4, the method may include:
  • the execution process of S401 can refer to the execution process of S201, and no more details are provided here.
  • S402. Determine whether the data to be detected is safe data according to the attribute information of the data to be detected.
  • the type of data to be detected includes at least one of text type, image type, voice type, or video type.
  • the type of data to be detected may also include other types, which is not limited in the embodiments of the present disclosure.
  • the preset models corresponding to multiple data types can be learned in advance, and the preset models corresponding to multiple data types can be stored.
  • the preset model is obtained according to the type of data to be detected.
  • S405. Perform security verification on the attribute information and/or the data to be detected through a preset model, and determine whether the data to be detected is safe data.
  • the preset model is learned based on the first sample (attribute information of the detected data)
  • the preset model is used to perform security verification on the attribute information of the detected data to determine whether the data to be tested is Safety data.
  • the attribute sample data includes known security data attributes and known risk data attributes.
  • the preset model is learned based on the second sample (the detected data)
  • the preset model is used to perform security verification on the detected data to determine whether the data to be detected is safe data.
  • Attribute sample data includes known safety data and known risk data.
  • the preset model when the preset model is learned based on the first sample (attribute information of the detected data) and the second sample (detected data), the preset model
  • the attribute information is used for security verification to determine whether the data to be detected is safe data.
  • the attribute sample data includes known safety data, known safety data attribute information, known risk data, and known risk data attribute information.
  • the attribute information of the data to be detected is firstly subjected to a rough security verification of the data to be detected.
  • the verification process is simple and convenient.
  • the security of the data to be tested is verified through a preset model. Since the preset model is obtained by learning the sample data through the neural network, the sample data includes the marked safety sample data and the marked risk sample data, therefore, according to the preset model, it can accurately verify whether the data to be tested is safe. In the above process In addition to saving labor costs, it can also improve detection efficiency and accuracy.
  • the data detection device 10 may include a detection module 11;
  • the detection module 11 is configured to detect the security of the data to be detected according to the attribute information of the data to be detected and a preset model, and the preset model is obtained by learning the sample data through a neural network.
  • the data detection apparatus provided by the embodiments of the present disclosure may execute the technical solutions shown in the foregoing method embodiments.
  • the implementation principles and beneficial effects are similar, and details are not described herein again.
  • the data detection device 10 further includes a first acquisition module 12;
  • the first acquiring module 12 is configured to acquire attribute information of the data to be detected.
  • the attribute information of the data to be detected includes at least one of the following attributes: a format of the data to be detected, a resource used by the data to be detected, and a frequency of data transmission.
  • the data transmission frequency is a frequency at which the device that sends the data to be detected sends data within a preset time period.
  • the detection module 11 is configured to:
  • the data to be detected is safety data according to the attribute information
  • the device further comprises a determination module 13, and the determination module configuration 13 is:
  • the detection module 11 detects that the data to be detected is risk data according to the attribute information, it is determined that the data to be detected is risk data.
  • the detection module 11 is configured to:
  • the security range corresponding to the format of the data to be detected includes at least one preset security format.
  • the security range corresponding to the resource used by the data to be detected includes a preset resource range.
  • the safety range corresponding to the data transmission frequency includes a preset frequency range.
  • the data detection device 10 further includes a second acquisition module 14 and a third acquisition module 15,
  • the second acquiring module 14 is configured to acquire the type of the data to be detected, and the type of the data to be detected includes at least one of a text type, an image type, a voice type, or a video type;
  • the third obtaining module 15 is configured to obtain the preset model according to the type of the data to be detected
  • the detection module configuration 11 is to perform security verification on the attribute information and/or the data to be detected through the preset model.
  • the sample data includes marked safety sample data and marked risk sample data.
  • the data detection apparatus provided by the embodiments of the present disclosure can execute the technical solutions shown in the foregoing method embodiments, and the implementation principles and beneficial effects are similar, and details are not described herein again.
  • An embodiment of the present disclosure also provides a computer including the above-mentioned data detection device.
  • An embodiment of the present disclosure also provides a computer-readable storage medium that stores computer-executable instructions that are configured to perform the above-described data detection method.
  • An embodiment of the present disclosure also provides a computer program product.
  • the computer program product includes a computer program stored on a computer-readable storage medium.
  • the computer program includes program instructions. When the program instructions are executed by a computer, the The computer executes the above data detection method.
  • the aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.
  • the electronic device 20 includes:
  • At least one processor 21, one processor 21 is taken as an example in FIG. 7; and the memory 22 may further include a communication interface 23 and a bus 24. Among them, the processor 21, the communication interface 23, and the memory 22 can communicate with each other through the bus 24. The communication interface 24 can be used for information transmission.
  • the processor 21 may call logic instructions in the memory 22 to execute the data detection method of the above embodiment.
  • logic instructions in the above-mentioned memory 22 may be implemented in the form of software functional units and sold or used as an independent product, and may be stored in a computer-readable storage medium.
  • the memory 22 is a computer-readable storage medium and can be used to store software programs and computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure.
  • the processor 21 executes functional applications and data processing by running software programs, instructions, and modules stored in the memory 22, that is, implementing the data detection method in the foregoing method embodiments.
  • the memory 22 may include a storage program area and a storage data area, where the storage program area may store an operating system and application programs required for at least one function; the storage data area may store data created according to the use of a terminal device and the like.
  • the memory 22 may include a high-speed random access memory, and may also include a non-volatile memory.
  • the technical solutions of the embodiments of the present disclosure may be embodied in the form of software products, which are stored in a storage medium and include one or more instructions to make a computer device (which may be a personal computer, server, or network) Equipment, etc.) to perform all or part of the steps of the method described in the embodiments of the present disclosure.
  • the aforementioned storage medium may be a non-transitory storage medium, including: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.
  • a medium that can store program codes may also be a transient storage medium.
  • first, second, etc. may be used in this application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another.
  • the first element can be called the second element, and likewise, the second element can be called the first element, as long as all occurrences of the "first element” are consistently renamed and all occurrences of The “second component” can be renamed consistently.
  • the first element and the second element are both elements, but they may not be the same element.
  • the various aspects, implementations, implementations or features in the described embodiments can be used alone or in any combination.
  • Various aspects in the described embodiments may be implemented by software, hardware, or a combination of software and hardware.
  • the described embodiments may also be embodied by a computer-readable medium that stores computer-readable code including instructions executable by at least one computing device.
  • the computer-readable medium can be associated with any data storage device capable of storing data, which can be read by a computer system.
  • Computer-readable media used for examples may include read-only memory, random access memory, CD-ROM, HDD, DVD, magnetic tape, optical data storage devices, and the like.
  • the computer-readable medium may also be distributed in computer systems connected through a network, so that computer-readable codes can be stored and executed in a distributed manner.

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  • Computer Security & Cryptography (AREA)
  • Computer Networks & Wireless Communication (AREA)
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Abstract

L'invention concerne un procédé, un appareil et un dispositif de détection de données. Le procédé suppose de détecter la sécurité de données à détecter en fonction d'informations sur des attributs des données à détecter et d'un modèle prédéfini (S202). Le modèle prédéfini est obtenu par apprentissage de données d'échantillons au moyen d'un réseau neuronal. L'efficacité et la précision de détection de données s'en trouvent accrues.
PCT/CN2018/119060 2018-12-04 2018-12-04 Procédé, appareil et dispositif de détection de données WO2020113401A1 (fr)

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PCT/CN2018/119060 WO2020113401A1 (fr) 2018-12-04 2018-12-04 Procédé, appareil et dispositif de détection de données
CN201880098312.7A CN113316921A (zh) 2018-12-04 2018-12-04 数据检测方法、装置及设备

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CN111914543A (zh) * 2020-06-20 2020-11-10 中国建设银行股份有限公司 报表合法性检测方法、装置、电子设备及可读存储介质

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CN102195975A (zh) * 2011-04-08 2011-09-21 上海电机学院 基于移动代理和学习向量量化神经网络的智能nips架构
CN107454097A (zh) * 2017-08-24 2017-12-08 深圳中兴网信科技有限公司 异常访问的检测方法、系统、计算机设备、可读存储介质
CN107888571A (zh) * 2017-10-26 2018-04-06 江苏省互联网行业管理服务中心 一种基于HTTP日志的多维度webshell入侵检测方法及检测系统

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CN102195975A (zh) * 2011-04-08 2011-09-21 上海电机学院 基于移动代理和学习向量量化神经网络的智能nips架构
CN107454097A (zh) * 2017-08-24 2017-12-08 深圳中兴网信科技有限公司 异常访问的检测方法、系统、计算机设备、可读存储介质
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111914543A (zh) * 2020-06-20 2020-11-10 中国建设银行股份有限公司 报表合法性检测方法、装置、电子设备及可读存储介质

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