Encryption method for urban safety heterogeneous sensor network based on chaotic neural network

Abstract

The present invention discloses an encryption method for a city safety heterogeneous sensor network based on a chaotic neural network, and relates to the field of city data encryption technology, including collecting city data through a city safety heterogeneous sensor network; constructing a chaotic neural network, encrypting and transmitting the collected city data through a public key; a city data center receives the encrypted and transmitted city data, decrypts the encrypted content through a private key to re-form the city data; the city data center verifies the integrity of the decrypted city data, and stores the data in a city database. The present invention has the following beneficial effects: the present invention uses a chaotic neural network to encrypt and decrypt the city data obtained by the city safety heterogeneous sensor network, thereby greatly improving the security of encryption, being able to resist complex attack methods, and simultaneously ensuring the efficiency and accuracy of transmission, which helps to promote the process of urban intelligent construction.

Description

Technical Field

[0001] The invention relates to the technical field of urban data encryption, in particular to an encryption method for an urban safety heterogeneous sensor network based on a chaotic neural network. Background Art

[0002] With the acceleration of urbanization, urban security heterogeneous sensor networks have gradually become an important part of modern urban infrastructure. These sensor networks collect various types of data in real time through sensors installed throughout the city, such as traffic flow, environmental quality, energy consumption, etc., and transmit these data to the city data center for analysis and processing. However, since these data may contain a large amount of sensitive information, how to ensure the security and privacy of data during transmission has become an urgent problem that needs to be solved. Although traditional encryption technologies can meet these requirements to a certain extent, they are often difficult to resist advanced attack methods and may have a negative impact on the efficiency of data transmission and processing. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing encryption methods for urban safety heterogeneous sensor networks based on chaotic neural networks, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that although traditional encryption technologies can meet these requirements to a certain extent, they are often difficult to resist advanced attack methods and may have a negative impact on the efficiency of data transmission and processing.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: an encryption method for a city safety heterogeneous sensor network based on a chaotic neural network, which includes collecting city data through a city safety heterogeneous sensor network;

[0006] Construct a chaotic neural network to encrypt and transmit the collected city data through public key;

[0007] The city data center receives the encrypted transmitted city data, decrypts the encrypted content using the private key and re-forms the city data;

[0008] The city data center verifies and decrypts the integrity of the city data and stores the data in the city database.

[0009] As a preferred solution of the urban safety heterogeneous sensor network encryption method based on chaotic neural network described in the present invention, wherein: the urban safety heterogeneous sensor network collects urban data in real time through the urban sensor network, and the urban sensor network is composed of sensors installed in the city, and all sensors work together to monitor the city in real time.

[0010] As a preferred solution of the urban safety heterogeneous sensor network encryption method based on chaotic neural network of the present invention, wherein: the chaotic neural network is constructed and stored in the urban data center, and the construction of the chaotic neural network includes:

[0011] Q1. Generate a chaotic system, the formula algorithm is as follows:

[0012]

[0013]

[0014]

[0015] Where σ, ρ and β are the parameters of the chaotic system, t is the time, and x, y and z are the input and output values ​​of the chaotic system;

[0016] Q2. Construct a neural network. The neural network consists of multiple layers, and its formula is as follows:

[0017] y (l) =f(W l x l-1 +b l )

[0018] Where f is the activation function, x is the input value, W is the weight value, b is the bias, and l is the number of neural network layers;

[0019] Q3. Train the neural network. First, given the input value x, get the final predicted value y^. The formula is as follows:

[0020] y^=f(Wx+b)

[0021] Then the neural network training loss is calculated:

[0022]

[0023] Where N is the number of samples, and then back propagation calculation is performed. The weight gradient of the lth layer is expressed as:

[0024]

[0025] Finally, the neural network weights and biases are updated:

[0026]

[0027]

[0028] Among them, α is the learning rate during neural network training. The construction and training of the chaotic neural network are completed through the above steps.

[0029] As a preferred solution of the encryption method of the urban safety heterogeneous sensor network based on chaotic neural network described in the present invention, wherein: the chaotic neural network generates a secret key sequence to form a public key, and the urban safety heterogeneous sensor network receives the public key sent by the urban data center to encrypt the collected urban data:

[0030]

[0031] Where P is the original urban data collected by the urban safety heterogeneous sensor network, K public is the chaotic encryption public key generated by the chaotic neural network, C is the encrypted city data, and the urban security heterogeneous sensor network uses the public key to encrypt the data and transmits the encrypted data to the city data center.

[0032] As a preferred solution of the urban safety heterogeneous sensor network encryption method based on chaotic neural network of the present invention, wherein: after receiving the urban data encrypted by the urban safety heterogeneous sensor network, the urban data center decrypts the data by using the private key of the same chaotic sequence:

[0033]

[0034] Where P' is the decrypted data, K secret The decryption private key generated for the chaotic neural network.

[0035] As a preferred solution of the encryption method of the urban safety heterogeneous sensor network based on chaotic neural network of the present invention, wherein: the urban data center verifies the integrity of the decrypted data, including:

[0036] If the decrypted data is the same as the original data collected by the urban safety heterogeneous sensor network, the accuracy of the decrypted data is confirmed and the data is stored;

[0037] If there is a difference between the decrypted data and the original data collected by the urban safety heterogeneous sensor network, the encrypted data is decrypted again and the public key and private key generated by the chaotic neural network are compared. The chaotic neural network is self-checked simultaneously to find out the cause of the difference and make corrections.

[0038] As a preferred solution of the urban safety heterogeneous sensor network encryption method based on chaotic neural network described in the present invention, the storing of data in the database includes cleaning the decrypted data after data structuring, archiving and classifying the data according to the specific identification of the data, using the additional encryption measures provided by the chaotic neural network algorithm, re-encrypting the key information or sensitive information in the database, and regularly backing up the data in the database.

[0039] As a preferred solution of the urban safety heterogeneous sensor network encryption method based on chaotic neural network described in the present invention, the urban data center regularly detects and maintains the database, urban safety heterogeneous sensor network and chaotic neural network, and synchronously records all detection and maintenance records in the database, and the staff can query all historical records through the database.

[0040] A computer device comprises: a memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of an encryption method for a city safety heterogeneous sensor network based on a chaotic neural network are implemented.

[0041] A computer-readable storage medium stores a computer program, characterized in that when the computer program is executed by a processor, the computer program implements the steps of an encryption method for a city safety heterogeneous sensor network based on a chaotic neural network.

[0042] The beneficial effects of the present invention are as follows: the present invention uses a chaotic neural network to encrypt, decrypt and transmit urban data obtained by a city security heterogeneous sensor network, thereby greatly improving the security of encryption, being able to resist complex attack methods, and simultaneously ensuring the efficiency and accuracy of transmission, which helps to promote the progress of urban intelligent construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0044] Figure 1 The figure is a flow chart of the encryption method for urban security heterogeneous sensor networks based on chaotic neural networks.

[0045] Figure 2 Schematic diagram of equipment for encryption method of urban security heterogeneous sensor network based on chaotic neural network. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0049] Example 1

[0050] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides an encryption method for a city safety heterogeneous sensor network based on a chaotic neural network. The encryption method for a city safety heterogeneous sensor network based on a chaotic neural network includes the following steps:

[0051] S1, collect urban data through urban safety heterogeneous sensor network;

[0052] It should be noted that the urban safety heterogeneous sensor network collects urban data in real time through the urban sensor network. The urban sensor network is composed of sensors installed in the city, and all sensors work together to monitor the city in real time.

[0053] S2, construct a chaotic neural network and encrypt and transmit the collected city data through the public key;

[0054] It should be noted that the chaotic neural network is constructed and stored in the city data center. The construction of the chaotic neural network includes:

[0055] Q1. Generate a chaotic system, the formula algorithm is as follows:

[0056]

[0057]

[0058]

[0059] Where σ, ρ and β are the parameters of the chaotic system, t is the time, and x, y and z are the input and output values ​​of the chaotic system;

[0060] Q2. Construct a neural network. The neural network consists of multiple layers, and its formula is as follows:

[0061] y (l) =f(W l x l-1 +b l )

[0062] Where f is the activation function, x is the input value, W is the weight value, b is the bias, and l is the number of neural network layers;

[0063] Q3. Train the neural network. First, given the input value x, get the final predicted value y^. The formula is as follows:

[0064] y^=f(Wx+b)

[0065] Then the neural network training loss is calculated:

[0066]

[0067] Where N is the number of samples, and then back propagation calculation is performed. The weight gradient of the lth layer is expressed as:

[0068]

[0069] Finally, the neural network weights and biases are updated:

[0070]

[0071]

[0072] Among them, α is the learning rate during neural network training. The construction and training of the chaotic neural network are completed through the above steps.

[0073] Furthermore, the chaotic neural network generates a secret key sequence to form a public key. The urban safety heterogeneous sensor network receives the public key sent by the urban data center to encrypt the collected urban data:

[0074]

[0075] Where P is the original urban data collected by the urban safety heterogeneous sensor network, K public is the chaotic encryption public key generated by the chaotic neural network, C is the encrypted city data, the city security heterogeneous sensor network uses the public key to encrypt the data and transmits the encrypted data to the city data center

[0076] S3, the city data center receives the encrypted transmitted city data, decrypts the encrypted content using the private key to re-form the city data;

[0077] It should be noted that after receiving the encrypted city data from the city security heterogeneous sensor network, the city data center decrypts the data using the private key of the same chaotic sequence:

[0078]

[0079] Where P' is the decrypted data, K secret The decryption private key generated for the chaotic neural network.

[0080] S4. The city data center verifies and decrypts the integrity of the city data and stores the data in the city database;

[0081] It should be noted that the city data center verifies the integrity of the decrypted data, including:

[0082] If the decrypted data is the same as the original data collected by the urban safety heterogeneous sensor network, the accuracy of the decrypted data is confirmed and the data is stored;

[0083] If there is a difference between the decrypted data and the original data collected by the urban safety heterogeneous sensor network, the encrypted data is decrypted again and the public key and private key generated by the chaotic neural network are compared. The chaotic neural network is self-checked simultaneously to find out the cause of the difference and make corrections.

[0084] It should also be noted that storing data in a database includes cleaning the decrypted data after data structuring, archiving and classifying the data according to the specific identifiers of the data, using additional encryption measures provided by the chaotic neural network algorithm, re-encrypting key information or sensitive information in the database, and regularly backing up the data in the database.

[0085] Furthermore, the city data center regularly detects and maintains the database, the city safety heterogeneous sensor network and the chaotic neural network, and synchronizes all detection and maintenance records in the database. The staff can query all historical records through the database.

[0086] Example 2

[0087] This is the second embodiment of the present invention. This embodiment is different from the previous embodiment in that it further includes:

[0088] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0089] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0090] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0091] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0092] Example 3

[0093] This is the third embodiment of the present invention. This embodiment is different from the previous two embodiments. In order to verify the beneficial effects of the present invention, experiments are carried out and the results are shown in the following table.

[0094] Table 1: Comparison between the method of the present invention and the traditional method

[0095]

[0096]

[0097] Through the comparison of the above results, it can be concluded that the method of the present invention is superior to the traditional method in the encrypted transmission of urban security data, and can play a good role in protecting the urban data transmission process. At the same time, it is also superior to the traditional method in terms of accuracy, which is helpful to promote the progress of urban intelligent construction.

[0098] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An encryption method for urban safety heterogeneous sensor networks based on chaotic neural networks, characterized by: include, Collect urban data through urban safety heterogeneous sensor networks; Construct a chaotic neural network to encrypt and transmit the collected city data through public key; The city data center receives the encrypted transmitted city data, decrypts the encrypted content using the private key and re-forms the city data; The city data center verifies and decrypts the integrity of the city data and stores the data in the city database; The chaotic neural network is constructed and stored in the city data center. The construction of the chaotic neural network includes: Q1. Generate a chaotic system, the formula algorithm is as follows: Where σ, ρ and β are the parameters of the chaotic system, t is the time, and x, y and z are the input and output values ​​of the chaotic system; Q2. Construct a neural network. The neural network consists of multiple layers, and its formula is as follows: y (l) =f(W l x l-1 +b l ) Where f is the activation function, x is the input value, W is the weight value, b is the bias, and l is the number of neural network layers; Q3. Train the neural network. First, give the input value x and get the final predicted value y. ∧ , the formula is as follows: y ∧ =f(Wx+b) Then the neural network training loss is calculated: Where N is the number of samples, and then back propagation calculation is performed. The weight gradient of the lth layer is expressed as: Finally, the neural network weights and biases are updated: Among them, α is the learning rate during neural network training. The construction and training of the chaotic neural network are completed through the above steps; The city safety heterogeneous sensor network collects city data in real time through the city sensor network. The city sensor network is composed of sensors installed in the city. All sensors work together to monitor the city in real time. The chaotic neural network generates a secret key sequence to form a public key. The urban safety heterogeneous sensor network receives the public key sent by the urban data center to encrypt the collected urban data: Where P is the original urban data collected by the urban safety heterogeneous sensor network, K public is the chaotic encryption public key generated by the chaotic neural network, C is the encrypted city data, and the urban security heterogeneous sensor network uses the public key to encrypt the data and transmits the encrypted data to the city data center.

2. The encryption method for urban safety heterogeneous sensor network based on chaotic neural network as claimed in claim 1 is characterized by: After receiving the encrypted city data from the city security heterogeneous sensor network, the city data center decrypts the data using the private key of the same chaotic sequence: Where P' is the decrypted data, K secret The decryption private key generated for the chaotic neural network.

3. The encryption method for urban safety heterogeneous sensor network based on chaotic neural network as claimed in claim 2 is characterized by: The city data center verifies the integrity of the decrypted data, including: If the decrypted data is the same as the original data collected by the urban safety heterogeneous sensor network, the accuracy of the decrypted data is confirmed and the data is stored; If there is a difference between the decrypted data and the original data collected by the urban safety heterogeneous sensor network, the encrypted data is decrypted again and the public key and private key generated by the chaotic neural network are compared. The chaotic neural network is self-checked simultaneously to find out the cause of the difference and make corrections.

4. The encryption method for urban safety heterogeneous sensor network based on chaotic neural network as claimed in claim 3 is characterized by: The storing of data in the database includes cleaning the decrypted data after data structuring, archiving and classifying the data according to the specific identification of the data, using the additional encryption measures provided by the chaotic neural network algorithm, re-encrypting the key information or sensitive information in the database, and regularly backing up the data in the database.

5. The encryption method for urban safety heterogeneous sensor network based on chaotic neural network as claimed in claim 4 is characterized by: The city data center regularly detects and maintains the database, the city safety heterogeneous sensor network and the chaotic neural network, and synchronously records all the detection and maintenance records in the database, and the staff can query all the historical records through the database.

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