An Internet of Things smart device identity authentication method and system

Abstract

The present invention provides an Internet of Things smart device identity authentication method and system, which are applied to the field of Internet of Things technology. The method includes: through step one, determining the target smart device and the target mobile terminal. Step two, generating a first authentication key according to the target smart device and the target mobile terminal. Step three, based on the Internet of Things, collecting and obtaining the usage frequency information of the target smart device within a preset time range. Step four, inputting the usage frequency information into a frequency stability analysis model to obtain a first deadline. Step five, using the first authentication key in the target mobile terminal to perform identity authentication on the target smart device. Step six, when the usage time of the first authentication key reaches the first deadline, the first authentication key becomes invalid, the authentication key is regenerated, and steps two to six are repeated. The technical problem that the authentication method of the Internet of Things in the prior art has low security, resulting in easy leakage of data is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an identity authentication method and system for an Internet of Things smart device. Background Art

[0002] The Internet of Things refers to the real-time collection of various types of information required through various information sensors, and the interconnection and connection of information between objects by connecting various devices to the network. Identity authentication is the basis of secure and trusted data in the Internet of Things system. Without identity authentication, trust between systems cannot be established. However, in the existing technology, the authentication method of the Internet of Things mostly uses a fixed username and password for authentication, and the password is easily leaked during use, resulting in low security of the authentication method of the Internet of Things, which in turn causes the problem of important data leakage.

[0003] Therefore, there is a technical problem in the prior art that the authentication method of the Internet of Things is less secure, resulting in data being easily leaked. Summary of the invention

[0004] The present application provides an IoT smart device identity authentication method and system, which is used to solve the technical problem in the prior art that the IoT authentication method has low security and causes data to be easily leaked.

[0005] In view of the above problems, the present application provides an IoT smart device identity authentication method and system.

[0006] The first aspect of the present application provides an Internet of Things smart device identity authentication method, the method comprising: step one, determining a target smart device and a target mobile terminal; step two, generating a first authentication key based on the target smart device and the target mobile terminal; step three, based on the Internet of Things, collecting and acquiring the usage frequency of the target smart device within a preset time range to obtain usage frequency information; step four, inputting the usage frequency information into a frequency stability analysis model to obtain a first time limit; step five, using the first authentication key in the target mobile terminal to perform identity authentication of the target smart device; step six, when the usage time of the first authentication key reaches the first time limit, the first authentication key becomes invalid, the authentication key is regenerated, and steps two to six are repeated.

[0007] The second aspect of the present application provides an Internet of Things smart device identity authentication system, the system comprising: a device acquisition module, used to execute step one, determine the target smart device and the target mobile terminal; a first authentication key generation module, used to execute step two, generate a first authentication key according to the target smart device and the target mobile terminal; a usage frequency information acquisition module, used to execute step three, based on the Internet of Things, collect and acquire the usage frequency of the target smart device within a preset time range, and obtain usage frequency information; a first deadline acquisition module, used to execute step four, input the usage frequency information into a frequency stability analysis model, and obtain a first deadline; an identity authentication module, used to execute step five, use the first authentication key in the target mobile terminal to authenticate the identity of the target smart device; a loop execution module, used to execute step six, when the usage time of the first authentication key reaches the first deadline, the first authentication key becomes invalid, the authentication key is regenerated, and steps two to six are repeated.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The method provided by the embodiment of the present application determines the target smart device and the target mobile terminal. According to the target smart device and the target mobile terminal, a first authentication key is generated. Based on the Internet of Things, the usage frequency information of the target smart device within a preset time range is collected and obtained. A frequency stability analysis model is obtained, and the usage frequency information is input into the frequency stability analysis model to obtain a first time limit. Subsequently, the first authentication key is used in the target mobile terminal to authenticate the identity of the target smart device. When the usage time of the first authentication key reaches the first time limit, the first authentication key becomes invalid, the authentication key is regenerated, and the corresponding time limit is repeatedly set, and the key is obtained according to the time limit cycle. By obtaining the first authentication key, keys of different complexity are set for devices of different importance, which improves the security of identity authentication of important devices. At the same time, since the key is only valid within a certain time limit, the security of access authentication and data is further improved. The technical problem that the authentication method of the Internet of Things in the prior art is low in security, which makes data easy to be leaked, is solved.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of a method for authenticating an IoT smart device provided in this application;

[0012] Figure 2 A schematic diagram of a process for obtaining a first authentication key in an IoT smart device identity authentication method provided in this application;

[0013] Figure 3 A schematic diagram of a process for obtaining a first time limit in an IoT smart device identity authentication method provided by this application;

[0014] Figure 4 A schematic diagram of the structure of an IoT smart device identity authentication system is provided for this application.

[0015] Explanation of reference numerals: device acquisition module 11 , first authentication key generation module 12 , usage frequency information acquisition module 13 , first deadline acquisition module 14 , identity authentication module 15 , loop execution module 16 . DETAILED DESCRIPTION

[0016] The present application provides an IoT smart device identity authentication method and system, which is used to solve the technical problem in the prior art that the IoT authentication method has low security and causes data to be easily leaked.

[0017] The technical solution in this application will be described clearly and completely below with reference to the accompanying drawings. The implementation examples described are only part of the content that can be implemented in this application, rather than the entire content of this application.

[0018] Embodiment 1

[0019] like Figure 1 As shown, the present application provides an IoT smart device identity authentication method, the method comprising:

[0020] Step 1: Determine the target smart device and target mobile terminal;

[0021] Step 2: Generate a first authentication key according to the target smart device and the target mobile terminal;

[0022] Specifically, the Internet of Things refers to the real-time collection of various types of information required through various information sensors, and the realization of information exchange and connection between objects by connecting various devices to the network. Identity authentication is the basis of secure and trusted data in the Internet of Things system. Without identity authentication, trust between systems cannot be established. By determining the target smart device and the target mobile terminal, the target smart device is the smart device connected to the Internet of Things, and the target mobile terminal is the user's mobile terminal such as a mobile phone, computer and other mobile devices. Subsequently, based on the target smart device and the target mobile terminal, a first authentication key is generated, and the first authentication key is a key for mutual identification between the target smart device and the target mobile terminal.

[0023] like Figure 2As shown, step 2 of the method provided in the embodiment of the present application also includes:

[0024] Step 210: Collecting and obtaining importance information of the target smart device;

[0025] Step 220: inputting the importance information into a key generation analysis model to obtain an output result, wherein the output result includes complexity information;

[0026] Step 230: Generate the first authentication key according to the complexity information.

[0027] Specifically, the importance information of the target smart device is obtained, wherein the importance information of the target smart device includes the price of the target smart device, the confidentiality of the data therein, and other data. The importance information is input into the key generation analysis model, and the importance of the target smart device is obtained through analysis. The more important the target smart device is, the higher the complexity of its corresponding key is. Then, according to the complexity information of the target smart device, the first authentication key is generated, so that keys of different complexity are set for target smart devices of different importance, further improving the security of the target smart device.

[0028] The method step 220 provided in the embodiment of the present application also includes:

[0029] Step 221: Collect and obtain importance information of multiple smart devices to obtain a sample importance information set;

[0030] Step 222: Collect and obtain multiple complexity information for generating a key, and obtain a combination of sample complexity information;

[0031] Step 223: constructing the key generation analysis model using the sample importance information set and the sample complexity information set;

[0032] Step 224: Input the importance information into the key generation analysis model to obtain the output result.

[0033] Specifically, the importance information of different smart devices is collected to obtain an importance information set. Subsequently, multiple complexity information for generating keys is collected to obtain a combination of sample complexity information. Further, the key generation analysis model is constructed based on the sample importance information set and the sample complexity information set. The key generation analysis model is used to generate a key of corresponding complexity based on the importance information of the target smart device. Finally, the importance information is input into the constructed key generation analysis model to obtain the output result, wherein the output result is a key of a complexity corresponding to the importance information.

[0034] The method step 220 provided in the embodiment of the present application also includes:

[0035] Step 223-1: Identify the sample importance information set and the sample complexity information set to obtain construction data;

[0036] Step 223-2: constructing the key generation analysis model based on the neural network in machine learning;

[0037] Step 223-3: Use the constructed data to perform supervised training on the key generation analysis model until the key generation analysis model converges or reaches a preset accuracy, thereby obtaining the key generation analysis model.

[0038] Specifically, the sample importance information set and the sample complexity information set are identified, and the sample complexity information set corresponding to the identified sample importance information set is to correspond the importance and complexity of the elements in the importance information set and the sample complexity information set to each other, and obtain construction data. Subsequently, a key generation analysis model is constructed through a neural network model in machine learning. Furthermore, when constructing the key generation analysis model, the key generation analysis model is supervised and trained using the construction data. At this time, the key generation analysis model is an untrained neural network model. The supervised training is completed until the key generation analysis model converges or reaches a preset accuracy rate, and the key generation analysis model is obtained. At this time, the key generation analysis model has been trained, and the output result is obtained by inputting the importance information into the constructed key generation analysis model, wherein the output result is a key with a complexity corresponding to the importance information.

[0039] Step 3: Based on the Internet of Things, collect and obtain the usage frequency of the target smart device within a preset time range to obtain usage frequency information;

[0040] Step 4: input the usage frequency information into a frequency stability analysis model to obtain a first period;

[0041] Specifically, based on the Internet of Things, the usage frequency of the target smart device within a preset time range is collected to obtain usage frequency information. Subsequently, the usage frequency information is input into the frequency stability analysis model to obtain a first period, where the first period is the usage period of the key of the target smart device. Different usage periods of the key are obtained for different usage frequency information to further ensure the security of the key.

[0042] Step 5: Using the first authentication key to perform identity authentication of the target smart device in the target mobile terminal;

[0043] Step 6: When the usage time of the first authentication key reaches the first period, the first authentication key becomes invalid, a new authentication key is generated, and steps 2 to 6 are repeated.

[0044] Specifically, after completing the key setting, the target smart device is authenticated by the first authentication key in the target mobile terminal, so that the mobile terminal authenticates the target smart device. Subsequently, when the usage time of the first authentication key reaches the first period, that is, when the usage time of the key reaches the usage time limit, the first authentication key becomes invalid, and the mobile terminal device cannot authenticate the target smart device by the first authentication key. Then, the authentication key is regenerated, and steps 2 to 6 are repeated to further ensure the security of the key.

[0045] like Figure 3 As shown, step 4 of the method provided in the embodiment of the present application also includes:

[0046] Step 410: constructing the frequency stability analysis model;

[0047] Step 420: Input the usage frequency information into the frequency stability analysis model to obtain the first deadline.

[0048] Specifically, a frequency stability analysis model is constructed, wherein the frequency stability analysis model obtains the usage period of the corresponding key by analyzing the usage frequency of the target smart device, and the first period is obtained by inputting the usage frequency information into the frequency stability analysis model. When the usage frequency of the target smart device is higher, the first period corresponding to the device is shorter, thereby further improving the security of important devices.

[0049] The method step 410 provided in the embodiment of the present application also includes:

[0050] Step 411: Collect and obtain the usage frequencies of multiple target smart devices within the preset time range to obtain a sample usage frequency information set;

[0051] Step 412: Using the sample usage frequency information set as construction data, and based on a decision tree algorithm, construct the frequency stability analysis model.

[0052] Specifically, the usage frequencies of multiple target smart devices within a preset time range are collected, that is, the usage frequencies of multiple smart devices within a preset time range are obtained, and the usage frequencies within the preset time range are obtained to obtain a sample usage frequency information set. Subsequently, the sample usage frequency information set is used as construction data to construct a frequency stability analysis model through a decision tree algorithm. The frequency stability analysis model is used to output the usage period of the key according to the usage frequency of the smart device, so as to avoid the risk of the key being cracked due to the use of the key for too long a time, resulting in data leakage.

[0053] The method step 410 provided in the embodiment of the present application also includes:

[0054] Step 412-1: randomly selecting a sample usage frequency information from the sample usage frequency information set as a first segmentation threshold;

[0055] Step 412-2: constructing a first classification node of the frequency stability analysis model using the first segmentation threshold;

[0056] Step 412-3: randomly selecting a sample of usage frequency information from the usage frequency information set as a second segmentation threshold;

[0057] Step 412-4: constructing a second classification node of the frequency stability analysis model using the second segmentation threshold;

[0058] Step 412-5: Continue to construct the multi-level classification nodes of the frequency stability analysis model until the number of levels of the multi-level classification nodes reaches a preset value;

[0059] Step 412-6: Obtain multiple classification results according to the constructed multi-level classification nodes;

[0060] Step 412-7: Set multiple usage periods for the multiple classification results respectively to obtain the constructed frequency stability analysis model.

[0061] Specifically, when constructing the frequency stability analysis model, a sample usage frequency information is randomly selected from the sample usage frequency information set as the first segmentation threshold, and the first segmentation threshold is used to construct the first classification node of the frequency stability analysis model, that is, the usage frequency information of the first segmentation threshold is used as a classification node. Then, a sample usage frequency information is randomly selected from the usage frequency information set again as the second segmentation threshold. The second segmentation threshold is used to construct the second classification node of the frequency stability analysis model. Continue to construct the multi-level classification node of the frequency stability analysis model until the level of the multi-level classification node reaches a preset value, that is, obtain multiple different usage frequency information, obtain multiple segmentation thresholds, obtain multiple classification nodes according to the segmentation threshold, until the level of the multi-level classification node reaches the preset value, complete the multi-level classification node, and obtain multiple classification results. Then, multiple usage periods are set for the multiple classification results respectively, and the constructed frequency stability analysis model is obtained. The usage period of the key is obtained by constructing the frequency stability analysis model to avoid the risk of data leakage caused by the key being cracked due to long-term use of the key.

[0062] In summary, the method provided in the embodiment of the present application determines the target smart device and the target mobile terminal. According to the target smart device and the target mobile terminal, a first authentication key is generated. Based on the Internet of Things, the usage frequency information of the target smart device within a preset time range is collected and obtained. A frequency stability analysis model is obtained, and the usage frequency information is input into the frequency stability analysis model to obtain a first deadline. Subsequently, the first authentication key is used in the target mobile terminal to perform identity authentication on the target smart device. When the usage time of the first authentication key reaches the first deadline, the first authentication key becomes invalid, the authentication key is regenerated, and the corresponding deadline is repeatedly set, and the key is obtained according to the deadline cycle. By obtaining the first authentication key, keys of different complexities are set for devices of different importance, thereby improving the security of identity authentication of important devices. At the same time, since the key is only valid within a certain period, the security of access authentication and data is further improved. The technical problem that the authentication method of the Internet of Things in the prior art is low in security, which makes data easy to be leaked, is solved.

[0063] Embodiment 2

[0064] Based on the same inventive concept as the method for authenticating an IoT smart device in the aforementioned embodiment, Figure 4 As shown, the present application provides an IoT smart device identity authentication system, the system comprising:

[0065] The device acquisition module 11 is used to execute step 1, determining the target smart device and the target mobile terminal;

[0066] A first authentication key generation module 12 is used to execute step 2, generating a first authentication key according to the target smart device and the target mobile terminal;

[0067] The usage frequency information acquisition module 13 is used to execute step 3, based on the Internet of Things, collect and acquire the usage frequency of the target smart device within a preset time range to obtain the usage frequency information;

[0068] The first deadline acquisition module 14 is used to execute step 4, input the usage frequency information into the frequency stability analysis model to obtain the first deadline;

[0069] The identity authentication module 15 is used to execute step 5, using the first authentication key to perform identity authentication of the target smart device in the target mobile terminal;

[0070] The loop execution module 16 is used to execute step six: when the usage time of the first authentication key reaches the first period, the first authentication key becomes invalid, a new authentication key is generated, and steps two to six are repeatedly executed.

[0071] Furthermore, the first authentication key generation module 12 is also used for:

[0072] Collecting and obtaining importance information of the target smart device;

[0073] Inputting the importance information into a key generation analysis model to obtain an output result, wherein the output result includes complexity information;

[0074] The first authentication key is generated according to the complexity information.

[0075] Furthermore, the first authentication key generation module 12 is also used for:

[0076] Collect and obtain the importance information of multiple smart devices to obtain a sample importance information set;

[0077] Collect and obtain multiple complexity information for generating keys, and obtain a combination of sample complexity information;

[0078] Using the sample importance information set and the sample complexity information set to construct the key generation analysis model;

[0079] The importance information is input into the key generation analysis model to obtain the output result.

[0080] Furthermore, the first authentication key generation module 12 is also used for:

[0081] Identifying the sample importance information set and the sample complexity information set to obtain construction data;

[0082] Based on the neural network in machine learning, construct the key generation analysis model;

[0083] The key generation analysis model is supervised and trained using the construction data until the key generation analysis model converges or reaches a preset accuracy, thereby obtaining the key generation analysis model.

[0084] Furthermore, the first deadline acquisition module 14 is also used for:

[0085] Constructing the frequency stability analysis model;

[0086] The usage frequency information is input into the frequency stability analysis model to obtain the first deadline.

[0087] Furthermore, the first deadline acquisition module 14 is also used for:

[0088] Collect and obtain the usage frequencies of the plurality of target smart devices within the preset time range to obtain a sample usage frequency information set;

[0089] The frequency stability analysis model is constructed based on a decision tree algorithm using the sample usage frequency information set as construction data.

[0090] Furthermore, the first deadline acquisition module 14 is also used for:

[0091] Randomly selecting a sample usage frequency information from the sample usage frequency information set as a first segmentation threshold;

[0092] constructing a first classification node of the frequency stability analysis model using the first segmentation threshold;

[0093] Randomly selecting a sample of usage frequency information from the usage frequency information set again as a second segmentation threshold;

[0094] constructing a second classification node of the frequency stability analysis model using the second segmentation threshold;

[0095] Continue to construct the multi-level classification nodes of the frequency stability analysis model until the number of levels of the multi-level classification nodes reaches a preset value;

[0096] Obtaining multiple classification results according to the constructed multi-level classification nodes;

[0097] A plurality of usage periods are respectively set for the plurality of classification results to obtain the constructed frequency stability analysis model.

[0098] The above-mentioned embodiment 2 is used to execute the method in embodiment 1, and its execution principle and execution basis can be obtained through the content recorded in embodiment 1, and no further details are given here. Although the present application is described in conjunction with specific features and embodiments thereof, the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application, and the content obtained in this way also belongs to the scope of protection of the present application.

Claims

1. A method for authenticating an IoT smart device, characterized in that: The method comprises: Step 1: Determine the target smart device and target mobile terminal; Step 2: Generate a first authentication key according to the target smart device and the target mobile terminal; Step 3: Based on the Internet of Things, collect and obtain the usage frequency of the target smart device within a preset time range to obtain usage frequency information; Step 4: input the usage frequency information into a frequency stability analysis model to obtain a first period; Step 5: Using the first authentication key to perform identity authentication of the target smart device in the target mobile terminal; Step 6: When the usage time of the first authentication key reaches the first period, the first authentication key becomes invalid, a new authentication key is generated, and steps 2 to 6 are repeated; The step of generating a first authentication key according to the target smart device and the target mobile terminal includes: Collecting and obtaining importance information of the target smart device, the importance information including the price of the target smart device and the confidentiality level of the data therein; Inputting the importance information into a key generation analysis model to obtain an output result, wherein the output result includes complexity information; generating the first authentication key according to the complexity information; The step of inputting the importance information into the key generation analysis model includes: Collect and obtain the importance information of multiple smart devices to obtain a sample importance information set; Collect and obtain multiple complexity information of generated keys to obtain a sample complexity information set; Using the sample importance information set and the sample complexity information set to construct the key generation analysis model; Inputting the importance information into the key generation analysis model to obtain the output result; The key generation analysis model is constructed by using the sample importance information set and the sample complexity information set, including: Identifying the sample importance information set and the sample complexity information set to obtain construction data; Based on the neural network in machine learning, construct the key generation analysis model; The key generation analysis model is supervised and trained using the construction data until the key generation analysis model converges or reaches a preset accuracy, thereby obtaining the key generation analysis model.

2. The method according to claim 1, characterized in that Inputting the usage frequency information into a frequency stability analysis model to obtain a first term includes: Constructing the frequency stability analysis model; The usage frequency information is input into the frequency stability analysis model to obtain the first deadline.

3. The method according to claim 2, characterized in that Constructing the frequency stability analysis model includes: Collect and obtain the usage frequencies of the plurality of target smart devices within the preset time range to obtain a sample usage frequency information set; The frequency stability analysis model is constructed based on a decision tree algorithm using the sample usage frequency information set as construction data.

4. The method according to claim 3, characterized in that The frequency stability analysis model is constructed based on a decision tree algorithm using the sample usage frequency information set as construction data, including: Randomly selecting a sample usage frequency information from the sample usage frequency information set as a first segmentation threshold; constructing a first classification node of the frequency stability analysis model using the first segmentation threshold; Randomly selecting a sample of usage frequency information from the usage frequency information set again as a second segmentation threshold; constructing a second classification node of the frequency stability analysis model using the second segmentation threshold; Continue to construct the multi-level classification nodes of the frequency stability analysis model until the number of levels of the multi-level classification nodes reaches a preset value; Obtaining multiple classification results according to the constructed multi-level classification nodes; A plurality of usage periods are respectively set for the plurality of classification results to obtain the constructed frequency stability analysis model.

5. An IoT smart device identity authentication system, characterized in that: Used to execute an IoT smart device identity authentication method according to any one of claims 1 to 4, the system comprising: The device acquisition module is used to execute step 1, determine the target smart device and the target mobile terminal; A first authentication key generation module, used to execute step 2, generating a first authentication key according to the target smart device and the target mobile terminal; A usage frequency information acquisition module is used to execute step 3, based on the Internet of Things, collect and acquire the usage frequency of the target smart device within a preset time range to obtain the usage frequency information; A first deadline acquisition module, used to execute step 4, input the usage frequency information into a frequency stability analysis model to obtain a first deadline; An identity authentication module, configured to execute step 5, using the first authentication key to perform identity authentication of the target smart device in the target mobile terminal; The loop execution module is used to execute step six. When the usage time of the first authentication key reaches the first period, the first authentication key becomes invalid, a new authentication key is generated, and steps two to six are repeatedly executed.

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