CN116074123B - Method for safely transmitting digital information of Internet of things - Google Patents
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- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
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- H04L63/06—Network architectures or network communication protocols for network security for supporting key management in a packet data network
- H04L63/061—Network architectures or network communication protocols for network security for supporting key management in a packet data network for key exchange, e.g. in peer-to-peer networks
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- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0816—Key establishment, i.e. cryptographic processes or cryptographic protocols whereby a shared secret becomes available to two or more parties, for subsequent use
- H04L9/0838—Key agreement, i.e. key establishment technique in which a shared key is derived by parties as a function of information contributed by, or associated with, each of these
- H04L9/0841—Key agreement, i.e. key establishment technique in which a shared key is derived by parties as a function of information contributed by, or associated with, each of these involving Diffie-Hellman or related key agreement protocols
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Abstract
The invention discloses a method for safely transmitting digital information of the Internet of things, which relates to the technical field of data information transmission and comprises the following steps: receiving digital information from an internet-of-things data terminal and encrypting the digital information when the data terminal sends the digital information; step two: transmission of digital information; step three: monitoring and protecting the transmission process; step four: decrypting the transmitted digital information; step five: storing the decrypted digital information; step six: verifying the stored digital information; the digital information transmission safety is effectively protected through detection of a multi-azimuth and diversified firewall and an intrusion detection system linkage unit, so that the information safety of a user is ensured; the confidentiality and the security of digital information are improved by adopting a Diffie-Hellman key exchange algorithm model; the memory neural network algorithm is used for diagnosing fault information in the digital information transmission process, so that the digital information transmission fault diagnosis capability is improved.
Description
Technical Field
The invention relates to the technical field of data information transmission, in particular to a method for safely transmitting digital information of the Internet of things.
Background
Today, with the rapid development of society, networks are indispensible from daily life and working density of people, but the security problems of data storage, access control and transmission of the networks are more and more prominent while the working efficiency of people is improved. The existing terminal equipment, especially the terminal equipment of an access network, hardly solves the data security problem in transmission, and the data security protection degree in the industrial Internet of things is weaker, so that the risk of leakage or tampering of the data of the intelligent terminal or the embedded equipment exists in the transmission process. In recent years, network data security is more and more interesting, especially the rise of the internet of things, so that the life and the network of people are tighter, the core and the foundation of the internet of things are still the internet, the internet is an extended and expanded network on the basis of the internet, and the user end of the internet of things is extended and expanded to any article and article for information exchange and communication.
In general, digital information transmitted on the internet is not encrypted, so that problems such as interception, tampering, counterfeiting, repudiation of a sender and the like easily occur, and in the process of digital information transmission, information is easily revealed, so that unnecessary loss is caused. Even if the digital information is encrypted, people can carry out network attack on the transmission channel in the process of data exchange, and the digital information exchange is burdened. Based on the above, the invention discloses a method for safely transmitting digital information of the Internet of things.
Disclosure of Invention
Aiming at the defects of the technology, the invention discloses a method for safely transmitting digital information of the Internet of things, which can effectively protect the safety of the digital information transmission and ensure the information safety of users by detecting a firewall and an intrusion detection system linkage unit in multiple directions and multiple ways; the Diffie-Hellman key exchange algorithm model is adopted, a shared key is created by the requesting party and the target party together to encrypt the digital information, so that the confidentiality and the security of the digital information are improved; establishing a plurality of keys and carrying out key verification on the cloud service platform to determine whether the keys are correct; the memory neural network algorithm is used for diagnosing fault information in the digital information transmission process, interaction and communication of different digital information are realized, and the digital information transmission fault diagnosis capability is improved.
A method for safely transmitting digital information of the Internet of things comprises the following steps:
step one: receiving digital information from an internet-of-things data terminal and encrypting the digital information when the data terminal sends the digital information;
in the first step, a data encryption module is adopted to receive digital information; the data encryption module builds a Diffie-Hellman key exchange algorithm model to carry out encryption operation on the digital information; the data encryption module is used for acquiring target data to be transmitted; the data encryption module encrypts the target data by adopting a shared key to generate encrypted data; wherein the Diffie-Hellman key exchange algorithm model comprises a speed accelerator for increasing the encryption speed of the digital information and an information encoder for encoding the digital information.
Step two: transmission of digital information;
in the second step, the data transmission module transmits the encrypted digital information to the target node through the data transmission module; the data transmission module adopts a pre-established transmission channel to transmit the encrypted digital information and the encryption key;
step three: monitoring and protecting the transmission process;
in the third step, a firewall and an intrusion detection system linkage unit are adopted to monitor and protect target information in a transmission path in the transmission process; adopting a single LSTM block to realize safety fault diagnosis in the transmission process;
step four: decrypting the transmitted digital information;
in the fourth step, a data decryption module is adopted to decrypt the encrypted digital information transmitted to the target node through the shared secret key, and the original digital information is obtained; the data decryption module acquires a plurality of shared secret key verification original digital information, and compares the plurality of secret keys with the cloud service platform; the information is transmitted to the data storage module after error free; the data decryption module is used for decrypting the encrypted digital information to obtain original digital information;
step five: storing the decrypted digital information;
in the fifth step, the working state of the memory is switched by adopting a data storage module, and the original digital information is stored in the appointed memory; the data storage module is used for detecting a triggering mode of the memory;
Step six: verifying the stored digital information;
in step six, by verifying the digital information with the requesting party, it is confirmed that no other IP address is available in the transmission process.
As a further technical scheme of the invention, the method for safely transmitting the digital information of the Internet of things comprises the following steps of:
setting two globally disclosed parameters negotiated by a requesting party and a target party, wherein a prime number p and an integer g are a primitive root of p; the requesting party takes a private integer a and sends the private integer a to the target party to calculate the result: a=gamod p, the value of a is seen by other nodes; the target party takes a private integer b and sends the private integer b to the requesting party to calculate the result: b=gbmod p, the value of B is seen by other nodes; the requester calculates a symmetric encryption key k=bamod p= (gb) amod p=gabmod p; also the target party calculates a symmetric encryption key k=abmod p= (ga) bmod p=gabmod p;
as a further technical scheme of the invention, the method for safely transmitting the digital information of the Internet of things comprises the steps that a firewall and an intrusion detection system linkage unit are system linkage units which are arranged between networks and execute access control strategies; the firewall and intrusion detection system linkage unit comprises a firewall module, a system abnormality detection module, a system analysis and monitoring module, a system response detection module and an active scanning detection module; the firewall module is used for monitoring, limiting and changing digital information crossing the firewall, and externally shielding information, structures and operation conditions inside the network, and is provided with two management mechanisms for checking and preventing abnormal digital information from passing and allowing the digital information to be transmitted; the system anomaly detection module is used for monitoring and analyzing the encrypted digital information transmission in real time; the system analysis monitoring module is used for decoding the network port and the specific IP address, converting the decoding, and protecting the data interface meeting intrusion detection in real time; the system response detection module comprises an active monitoring module and a passive detection module; the active monitoring module is used for information automatic attack and active defense; timely defending according to the setting of the system by the user; the passive monitoring module feeds generated network attack information back to the user according to the analysis of the big data; the active scanning detection module adopts a circulating data filtering mode to continuously monitor and patrol system holes for 24 hours.
As a further technical scheme of the present invention, in the method for securely transmitting digital information of the internet of things, the data decryption module obtains a plurality of key verification original digital information through a network sharing port, compares a plurality of key output data information with standard data information set by a cloud service platform, and the comparison method includes:
step (1): the formula for determining the characteristic parameters of the key is as follows:
in the formula (1),G h represent the firsthEncryption rule parameters of the individual keys;Z h represent the firsthCharacter length of the individual keys;x hg represent the firsthFirst of the individual keysgCoefficients corresponding to the encryption elements of the individual characters;h=1，2，3，…，k，krepresenting the total number of keys;g=1，2，3，…，n，nthe total number of characters representing the key;
step (2): the cloud service platform determines the formula of the authentication parameter as follows:
in the formula (2),r f represent the firstfContent characteristics of the individual authentication rules;b f represent the firstfAuthentication weights of the authentication rules;qformat authentication parameters representing a cloud service center;F f represent the firstfMatching parameters of the authentication rules;
representing length authentication parameters of the cloud service platform;f=1，2，3，…，k；Crepresenting platform parameters set by a user; comparing and authenticating the acquired key characteristic parameters through the authentication parameters of the cloud service platform, and if the key characteristic parameters are the same as the authentication parameters of the cloud service platform, not changing the key; if the key characteristic parameter is different from the authentication parameter of the cloud service platform The key is modified and the replacement key is stolen by a third party during transmission.
As a further technical scheme of the invention, the method for safely transmitting the digital information of the internet of things comprises the following steps: the single LSTM block adopts a memory neural network algorithm model to realize fault diagnosis, wherein the working method of the memory neural network algorithm model comprises the following steps:
the digital information is input, deleted and read, so that the digital information processing is realized, the updating of a digital information base is continuously realized, and the screening capability of the digital information is improved; set C t Storing information for the digital information; f (f) t Removing information i from digital information t Digital information inflow information, O t Streaming information of the digital information;
extracting digital information characteristics output by a single LSTM block by adopting a Sigmoid function as the input of a fault detection model, and obtaining a model predicted value;
calculating a monolithic LSTM block output function, wherein the function formula is as follows:
in the formula (3), t represents different network node parameter data nodes in the neural network model, W [ i, f, C, O ] represents parameter weight matrixes in the neural network model, b [ i, f, C, O ] represents bias vectors of different node weight matrixes in the neural network model, X represents input digital information operation parameters, and Y represents digital information operation fault diagnosis data output parameters; k represents the digital information inflow speed; m represents abnormal information in the digital information; w represents the digital information outflow speed;
Reading a single LSTM block to output storage information, wherein the output function is as follows:
in the formula (4), tanh is expressed as hyperbolic tangent function operation, C t * Representing multiplications calculated by elements in neural network nodes。
And calculating an absolute value by taking a difference between the predicted value of the memory neural network model and the output storage information of the single LSTM block, judging whether the fault occurs, if the difference exists, the fault occurs, and if the difference does not exist, the fault does not occur. .
As a further technical scheme of the invention, the method for safely transmitting the digital information of the Internet of things is characterized in that the working state of a memory improves the capacity of the memory by setting a timing trigger mode and a interception trigger mode; the timing triggering mode switches the memory to a working state according to the time interval, and stores the original digital information into the memory; the interception trigger mode is used for intercepting the state of the transmission channel in real time; and if the transmission channel has data to be received, switching the memory to a working state, and storing the decrypted original data into the memory.
As a further technical scheme of the invention, the method for safely transmitting the digital information of the Internet of things comprises the steps that a speed accelerator comprises an input buffer zone module, an AI operator acceleration module and an output buffer zone; the input buffer zone module is used for receiving and buffering the digital information, reducing the interrupt frequency of the CPU and relaxing the corresponding time limit on the CPU interrupt; the AI operator acceleration module is used for accelerating the received digital information, and carrying out quick encryption processing on the received digital information through logic judgment, network calculation and network update of the AI operator; the output buffer area is used for outputting the encrypted digital information and relieving speed mismatch between the CPU and the IO equipment.
As a further technical scheme of the invention, the information encoder comprises an information receiving module, a Bayesian classification module, an information mapping module and an information output module; the information receiving module is used for receiving the transmitted digital information; the Bayesian classification module is used for training and classifying the received digital information; the information mapping module is used for carrying out information coding on the training classified digital information according to coding rules; the information output module is used for outputting the coded digital information; the output end of the information receiving module is connected with the input end of the Bayesian classification module, the output end of the Bayesian classification module is connected with the input end of the information mapping module, and the output end of the information mapping module is connected with the input end of the information output module.
As a further technical scheme of the invention, the working method of the game training algorithm GTA module is as follows:
the maximum safe operation threshold value of the current monitoring point is set as follows:
in the formula (5), the amino acid sequence of the compound,
maximum safe operation threshold value representing current monitoring data interaction node,/->
Representing the operation rule of the encrypted data,/->
Representing predicted risk value of monitoring point,/- >
Encryption data operation parameter of transmission data interaction node of Internet of things>
Indicating environmental influence factors to which data nodes are transmitted by the Internet of things (IOT)>
Represented is node argument, ++>
Representing the operating parameter argument, +.>
Representing environmental factor arguments;
and (3) carrying out game induction on node data by using a GTA algorithm, and recording a target function as shown in a formula (6):
in the formula (6), the amino acid sequence of the compound,
an objective function representing the input of the GTA algorithm, +.>
Representing a gaming function corresponding to pick node data, +.>
Representing the operation of a normal case node gaming function, +.>
Representing communication node parameters in the database, +.>
Representing the change of the device parameters in the game state;
the GTA algorithm reflects the game relation among individuals to a certain extent, and the game mode is shown as a formula (7):
in the formula (7), the amino acid sequence of the compound,
representing the designed individual game mode of the GTA algorithm, < ->
Representing individual node secure operation data, +.>
Representing node gaming restriction functions,/->
Representing constraint argument->
Represented is a normal function of the node game,
representing the operation standard of the transmission data interaction node of the Internet of things;
inputting the data of each node in the transmission data interaction node of the Internet of things into an algorithm program in a GTA algorithm game mode to obtain a final operation result, and then, assuming in the algorithm operation process 
Equation (8) can be obtained:
in the formula (8), the amino acid sequence of the compound,
indicating the result of the modified game after the GTA algorithm is operated, < + >>
Representing the post-game stability prediction value of the node +.>
Indicating an irresistible factor in the game process, < +.>
Indicating that each node of the data transmission interaction node of the Internet of things changes by electric energy,/-for example>
Representing different game group numbers, +.>
Representing the operation of a normal case node gaming function, +.>
Representing game coefficients +.>
The influence factor coefficients are shown;
finally, the method adjusts the reasons for the risk loopholes generated by different communication nodes, and the adjustment scheme is shown as a formula (9):
in the formula (9), the amino acid sequence of the compound,
indicating the optimal adjustment scheme deduced by the GTA algorithm, < + >>
Represents the maximum stable adjustment amount achieved by the adjustment function, < ->
Representing the actual stable adjustment amount, +.>
Representing the node stability variation function, +.>
Representing game outcome function, ++>
Representing the coefficient of variation variable, ">
And the adjusted game result after the GTA algorithm is operated is shown. The invention has the positive beneficial effects that compared with the prior art:
according to the invention, through multi-azimuth and diversified firewall and intrusion detection system linkage unit detection, digital information transmission safety is effectively protected, and information safety of users is ensured; the Diffie-Hellman key exchange algorithm model is adopted, a shared key is created by the requesting party and the target party together to encrypt the digital information, so that the confidentiality and the security of the digital information are improved; establishing a plurality of keys and carrying out key verification on the cloud service platform to determine whether the keys are correct; the memory neural network algorithm is used for diagnosing fault information in the digital information transmission process, interaction and communication of different digital information are realized, and the digital information transmission fault diagnosis capability is improved.
Drawings
For a clearer description of embodiments of the invention or of solutions in the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are only some embodiments of the invention, from which, without inventive faculty, other drawings can be obtained for a person skilled in the art, in which:
FIG. 1 is a flow chart of the steps of a method for secure transmission of digital information of the Internet of things according to the present invention;
FIG. 2 is a diagram showing the connection of the system structure of the firewall and the linkage unit of the intrusion detection system according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
As shown in fig. 1, a method for securely transmitting digital information of the internet of things comprises the following steps:
step one: receiving digital information from an internet-of-things data terminal and encrypting the digital information when the data terminal sends the digital information;
in the first step, a data encryption module is adopted to receive digital information; the data encryption module builds a Diffie-Hellman key exchange algorithm model to carry out encryption operation on the digital information; the data encryption module is used for acquiring target data to be transmitted; the data encryption module encrypts the target data by adopting a shared key to generate encrypted data; the Diffie-Hellman key exchange algorithm model comprises a speed accelerator and an information encoder, wherein the speed accelerator is used for improving the encryption speed of digital information, and the information encoder is used for encoding the digital information;
Step two: transmission of digital information;
in the second step, the data transmission module transmits the encrypted digital information to the target node through the data transmission module; the data transmission module adopts a pre-established transmission channel to transmit the encrypted digital information and the encryption key;
step three: monitoring and protecting the transmission process;
in the third step, a firewall and an intrusion detection system linkage unit are adopted to monitor and protect target information in a transmission path in the transmission process; adopting a single LSTM block to realize safety fault diagnosis in the transmission process;
step four: decrypting the transmitted digital information;
in the fourth step, a data decryption module is adopted to decrypt the encrypted digital information transmitted to the target node through the shared secret key, and the original digital information is obtained; the data decryption module acquires a plurality of shared secret key verification original digital information, and compares the plurality of secret keys with the cloud service platform; the information is transmitted to the data storage module after error free; the data decryption module is used for decrypting the encrypted digital information to obtain original digital information;
step five: storing the decrypted digital information;
in the fifth step, the working state of the memory is switched by adopting a data storage module, and the original digital information is stored in the appointed memory; the data storage module is used for detecting a triggering mode of the memory;
Step six: verifying the stored digital information;
in step six, by verifying the digital information with the requesting party, it is confirmed that no other IP address is available in the transmission process.
In the above embodiment, the Diffie-Hellman key exchange algorithm model encryption method is as follows:
setting two globally disclosed parameters negotiated by a requesting party and a target party, wherein a prime number p and an integer g are a primitive root of p; the requesting party takes a private integer a and sends the private integer a to the target party to calculate the result: a=gamod p, the value of a can be seen by other nodes; the target party takes a private integer b and sends the private integer b to the requesting party to calculate the result: b=gbmod p, the value of B can be seen by other nodes; the requester calculates a symmetric encryption key k=bamod p= (gb) amod p=gabmod p; the target party can also calculate a symmetric encryption key k=abmod p= (ga) bmod p=gabmod p;
through the above procedure, the requesting party and the target party have a common key k, and p, g, a, B are public parameters, and the specific values of a and B are not disclosed in view of the difficulty in calculating discrete logarithms, so that the key k is private.
In a specific embodiment, the Diffie-Hellman key exchange algorithm model is proposed by two persons, namely Diffie and Hellman; the Diffie-Hellman key exchange algorithm model allows two parties to commonly establish a shared key in an unsafe communication channel without knowing the other party in advance, and the shared key performs encryption protection on digital information.
In the above embodiment, the firewall and intrusion detection system linkage unit is a system linkage unit that is provided between networks to execute an access control policy; the firewall and intrusion detection system linkage unit comprises a firewall module, a system abnormality detection module, a system analysis and monitoring module, a system response detection module and an active scanning detection module; the firewall module is used for monitoring, limiting and changing digital information crossing the firewall, and externally shielding information, structures and operation conditions inside the network, and is provided with two management mechanisms for checking and preventing abnormal digital information from passing and allowing the digital information to be transmitted; the system anomaly detection module is used for monitoring and analyzing the encrypted digital information transmission in real time; the system analysis monitoring module is used for decoding the network port and the specific IP address, converting the decoding, and protecting the data interface meeting intrusion detection in real time; the system response detection module comprises an active monitoring module and a passive detection module; the active monitoring module is used for information automatic attack and active defense; timely defending according to the setting of the system by the user; the passive monitoring module feeds the generated network attack information back to the user according to the analysis of the big data, so that the user can distinguish whether security defense and detection are needed; the active scanning detection module adopts a circulating data filtering mode to continuously monitor and patrol system holes for 24 hours.
In a specific embodiment, monitoring of specific network protocol ports is enhanced, analysis and detection are carried out by protecting the protocol ports in a step-by-step manner and the sequence of the network protocol, and analyzing the characteristics of different protocol ports to realize different characteristic defense and detection; the scanning of the system loopholes is carried out by comparing the system security protection of big data with the system used by the user, and if the difference is found in the scanning and comparing processes, the user is enabled to carry out key investigation and judgment, and whether the problem of potential safety hazard of the system exists or not is enabled to enable the user to selectively carry out restoration. The firewall and the intrusion detection system are linked through an open interface to realize interaction, namely, the firewall or the intrusion detection system opens an interface for the other party to use, and the two parties communicate according to a fixed protocol to finish the detection of the encrypted digital information transmission channel; the linkage system does not affect the performance of the firewall and the intrusion detection system, and the two methods are better complemented.
In a specific embodiment, the steps of the firewall and the intrusion detection system linkage unit are as follows:
step 1: initializing a communication connection and the intrusion detection system initiates a connection to the firewall.
Step 2: when normal connection is established and the intrusion detection system generates a security event requiring notification of the firewall, the intrusion detection system transmits necessary interaction information to the firewall by sending a data packet in a contracted format.
Step 3: the firewall receives the interaction information, implements the interaction behavior, and feeds back the result to the intrusion detection system in the form of a data packet with a contracted format.
In the above embodiment, the data decryption module obtains a plurality of key verification original digital information through a network sharing port, and compares a plurality of key output data information with standard data information set by a cloud service platform, where the comparison method includes:
step (1): the formula for determining the characteristic parameters of the key is as follows:
in the formula (1),G h represent the firsthEncryption rule parameters of the individual keys;Z h represent the firsthCharacter length of the individual keys;x hg represent the firsthFirst of the individual keysgCoefficients corresponding to the encryption elements of the individual characters;h=1，2，3，…，k，krepresenting the total number of keys;g=1，2，3，…，n，nthe total number of characters representing the key; the formula (1) realizes the calculation of the characteristic parameters of the secret key; the characteristic parameters of the key are determined by calculating the encryption rule parameters and the character length of the key, and the authenticity of the key is ensured by comparing the characteristic parameters of the key with the cloud service platform.
Step (2): the cloud service platform determines the formula of the authentication parameter as follows:
in the formula (2),r f represent the firstfContent characteristics of the individual authentication rules;b f represent the firstfAuthentication weights of the authentication rules;qformat authentication parameters representing a cloud service center;F f represent the firstfMatching parameters of the authentication rules;
representing length authentication parameters of the cloud service platform;f=1，2，3，…，k；Crepresenting platform parameters set by a user; the authentication parameters are determined through the cloud service platform through the formula (2); the authentication parameters of the cloud service platform are obtained through the content characteristics of the authentication rules and the authentication weights of the authentication rules, the acquired key characteristic parameters are compared and authenticated through the authentication parameters of the cloud service platform, and if the key characteristic parameters are the same as the authentication parameters of the cloud service platform, the key is not availableChanging; if the key characteristic parameter is different from the authentication parameter of the cloud service platform, the key is modified and the replaced key is stolen by a third party in the transmission process.
In the above embodiment, the single LSTM block implements fault diagnosis by using a memory neural network algorithm model, where the working method of the memory neural network algorithm model includes:
the digital information is input, deleted and read, so that the digital information processing is realized, the updating of a digital information base is continuously realized, and the screening capability of the digital information is improved; set C t Storing information for the digital information; f (f) t Removing information i from digital information t Digital information inflow information, O t Streaming information of the digital information;
extracting digital information characteristics output by a single LSTM block by adopting a Sigmoid function as the input of a fault detection model, and obtaining a model predicted value;
calculating a monolithic LSTM block output function, wherein the function formula is as follows:
in the formula (3), t represents different network node parameter data nodes in the neural network model, W [ i, f, C, O ] represents parameter weight matrixes in the neural network model, b [ i, f, C, O ] represents bias vectors of different node weight matrixes in the neural network model, X represents input digital information operation parameters, and Y represents digital information operation fault diagnosis data output parameters; k represents the digital information inflow speed; m represents abnormal information in the digital information; w represents the digital information outflow speed; equation (3) enables computation of monolithic LSTM block output functions by invoking Sigmoid functions
Reading a single LSTM block to output storage information, wherein the output function is as follows:
in equation (4), tanh is expressed as a hyperbolic tangent function operation， C t * Representing multiplications calculated by elements in the neural network node. And (4) calculating the parameter weight matrix through a hyperbolic tangent function to realize reading of the output storage information of the single LSTM block. Calculating an absolute value by taking a difference between the predicted value of the memory neural network model and the output storage information of the single LSTM block, judging whether a fault occurs, and if the difference exists, judging that the fault occurs; no difference, no fault occurs.
In the above embodiment, the working state of the memory is set with a timing trigger mode and a listening trigger mode; the timing triggering mode switches the memory to a working state according to the time interval, and stores the original digital information into the memory; the timing trigger mode is timed by a timer, which is implemented by counting. The timer has a counter and a register TCNT inside, the counter being operated according to a clock. The counter counts once every other clock cycle, and the time of the timer is the count value of the counter x clock cycles. The timer is internally provided with 1 register TCNT, when the timing is started, a total count value is put into the TCNT register, then the value in the TCNT is automatically reduced by 1 every other clock period, when the value in the TCNT is reduced to 0, the TCNT triggers the timer to interrupt, the timing triggering mode is started, and the memory is switched to a working state; the interception trigger mode is used for intercepting the state of the transmission channel in real time; and if the transmission channel has data to be received, switching the memory to a working state, and storing the decrypted original data into the memory.
In the above embodiment, the speed accelerator includes an input buffer module, an AI operator acceleration module, and an output buffer; the input buffer zone module is used for receiving and buffering the digital information, reducing the interrupt frequency of the CPU and relaxing the corresponding time limit on the CPU interrupt; the AI operator acceleration module is used for accelerating the received digital information, and carrying out quick encryption processing on the received digital information through logic judgment, network calculation and network update of the AI operator; the output buffer area is used for outputting the encrypted digital information and relieving speed mismatch between the CPU and the IO equipment.
In a specific embodiment, the AI operator acceleration module comprises an AI calculation layer, which provides heterogeneous chip resources including high-performance GPU, kunlun and the like, high-performance RDMA or IB network, self-developed super AI computer X-MAN and the like; AI storage layer, including object storage BOS meeting data lake storage requirements, high performance parallel file system PFS designed specifically for AI; AI acceleration layer, including data lake storage acceleration suite RapidFS, AI Training acceleration suite AIAK-Training, AI reasoning acceleration suite; the input digital information is encrypted through the AI operator acceleration module, so that the encrypted digital information is expanded and stored, and the acceleration function of encrypting the digital information is realized.
In the above embodiment, the information encoder includes an information receiving module, a bayesian classification module, an information mapping module, and an information output module; the information receiving module is used for receiving the transmitted digital information; the Bayesian classification module is used for training and classifying the received digital information; the information mapping module is used for carrying out information coding on the training classified digital information according to coding rules; the information output module is used for outputting the coded digital information; the output end of the information receiving module is connected with the input end of the Bayesian classification module, the output end of the Bayesian classification module is connected with the input end of the information mapping module, and the output end of the information mapping module is connected with the input end of the information output module.
In a specific embodiment, the Bayesian classification module has the function of mapping the data items to be classified to a certain characteristic category, and both data classification and regression analysis can be used for classification, wherein the prediction refers to automatically giving popularization description of unknown data according to classification criteria from a sample-based data record so as to realize the prediction of the unknown data; bayesian classification is to assume that the impact of one attribute on a given classification is independent of other attributes; this assumption, called condition independent, greatly simplifies the computational effort required for classification.
The working method of the game training algorithm GTA module is as follows:
the maximum safe operation threshold value of the current monitoring point is set as follows:
in the formula (5), the amino acid sequence of the compound,
maximum safe operation threshold value representing current monitoring data interaction node,/->
Representing the operation rule of the encrypted data,/->
Representing predicted risk value of monitoring point,/->
Encryption data operation parameter of transmission data interaction node of Internet of things>
Indicating environmental influence factors to which data nodes are transmitted by the Internet of things (IOT)>
Represented is node argument, ++>
Representing the operating parameter argument, +.>
Representing environmental factor arguments;
the formula (1) mainly calculates the security node data through aggregation, the data is firstly processed through the game process of the maximum value in an algorithm mode, the optimal solution is found in a group node mode, and then the security value calculated by the algorithm is more accurate in a mode of converting the data after the collective game into two-to-two games. Such as interaction with nodes by adjacent nodes to effect data information transmission.
In the specific embodiment, the GTA can be recorded as a deep learning method, the GAT consists of a plurality of blocks with the same functions, the blocks are called Graph Attention Layer, and in the specific embodiment, the data information memory is improved through the Graph Attention Layer structure
And (3) carrying out game induction on node data by using a GTA algorithm, and recording a target function as shown in a formula (6):
in the formula (6), the amino acid sequence of the compound,
an objective function representing the input of the GTA algorithm, +.>
Representing a gaming function corresponding to pick node data, +.>
Representing the operation of a normal case node gaming function, +.>
Representing communication node parameters in the database, +.>
Representing a change in device parameters in the gaming state.
In actual operation, the GTA algorithm reflects the game relationship among individuals to a certain extent, and the game mode is shown in a formula (7):
in the formula (7), the amino acid sequence of the compound,
representing the designed individual game mode of the GTA algorithm, < ->
Representing individual node secure operation data, +.>
Representing node gaming restriction functions,/->
Representing constraint argument->
Represented is a normal function of the node game,
and the operation standard of the data interaction node transmitted by the Internet of things is represented.
Inputting the data of each node in the transmission data interaction node of the Internet of things into an algorithm program in a GTA algorithm game mode to obtain a final operation result, and then, assuming in the algorithm operation process
Equation (8) can be obtained:
in the formula (8), the amino acid sequence of the compound,
indicating the result of the modified game after the GTA algorithm is operated, < + >>
Representing the post-game stability prediction value of the node +.>
Indicating an irresistible factor in the game process, < +. >
Indicating that each node of the data transmission interaction node of the Internet of things changes by electric energy,/-for example>
Representing different game group numbers, +.>
Representing the operation of a normal case node gaming function, +.>
Representing game coefficients +.>
Shown are the influencing factor coefficients.
In the formula (9), the amino acid sequence of the compound,
indicating the optimal adjustment scheme deduced by the GTA algorithm, < + >>
Represents the maximum stable adjustment amount achieved by the adjustment function, < ->
Representing the actual stable adjustment amount, +.>
Representing the node stability variation function, +.>
Representing game outcome function, ++>
Representing the coefficient of variation variable, ">
And the adjusted game result after the GTA algorithm is operated is shown. And the GTA module improves the safe transmission and interaction capacity of the digital information of the Internet of things through a game training algorithm.
While specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are by way of example only, and that various omissions, substitutions, and changes in the form and details of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the above-described method steps to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is limited only by the following claims.
Claims (9)
1. The method for safely transmitting the digital information of the Internet of things is characterized by comprising the following steps of: the method comprises the following steps:
step one: receiving digital information from an internet-of-things data terminal and encrypting the digital information when the data terminal sends the digital information;
in the first step, a data encryption module is adopted to receive digital information; the data encryption module builds a Diffie-Hellman key exchange algorithm model to carry out encryption operation on the digital information; the data encryption module is used for acquiring target data to be transmitted; the data encryption module encrypts the target data by adopting a shared key to generate encrypted data; the Diffie-Hellman key exchange algorithm model comprises a speed accelerator and an information encoder, wherein the speed accelerator is used for improving the encryption speed of digital information, and the information encoder is used for encoding the digital information;
step two: transmission of digital information;
in the second step, the data transmission module transmits the encrypted digital information to the target node through the data transmission module; the data transmission module adopts a pre-established transmission channel to transmit the encrypted digital information and the encryption key;
step three: monitoring and protecting the transmission process;
in the third step, a firewall and an intrusion detection system linkage unit are adopted to monitor and protect target information in a transmission path in the transmission process; adopting a single LSTM block to realize safety fault diagnosis in the transmission process; wherein, the single LSTM block is provided with a game training algorithm GTA module;
Step four: decrypting the transmitted digital information;
in the fourth step, a data decryption module is adopted to decrypt the encrypted digital information transmitted to the target node through the shared secret key, and the original digital information is obtained; the data decryption module acquires a plurality of shared secret key verification original digital information, and compares the plurality of secret keys with the cloud service platform; the information is transmitted to the data storage module after error free; the data decryption module is used for decrypting the encrypted digital information to obtain the original digital information;
step five: storing the decrypted digital information;
in the fifth step, the working state of the memory is switched by adopting a data storage module, and the original digital information is stored in the appointed memory; the data storage module is used for detecting a triggering mode of the memory;
step six: verifying the stored digital information;
in step six, by verifying the digital information with the requesting party, it is confirmed that no other IP address is available in the transmission process.
2. The method for securely transmitting the digital information of the internet of things according to claim 1, wherein the method comprises the following steps: the Diffie-Hellman key exchange algorithm model encryption method comprises the following steps:
setting two globally disclosed parameters negotiated by a requesting party and a target party, wherein a prime number p and an integer g are a primitive root of p; the requesting party takes a private integer a and sends the private integer a to the target party to calculate the result: a=gamod p, the value of a is seen by other nodes; the target party takes a private integer b and sends the private integer b to the requesting party to calculate the result: b=gbmod p, the value of B is seen by other nodes; the requester calculates a symmetric encryption key k=bamod p= (gb) amod p=gabmod p; the target party also calculates a symmetric encryption key k=abmod p= (ga) bmod p=gabmod p.
3. The method for securely transmitting the digital information of the internet of things according to claim 1, wherein the method comprises the following steps:
the firewall and intrusion detection system linkage unit is a system linkage unit which is arranged between networks and used for executing access control strategies; the firewall and intrusion detection system linkage unit comprises a firewall module, a system abnormality detection module, a system analysis and monitoring module, a system response detection module and an active scanning detection module;
the firewall module is used for monitoring, limiting and changing digital information crossing the firewall, and is used for externally shielding information, structures and operation conditions inside the network, and is provided with two management mechanisms for checking and preventing abnormal digital information from passing and allowing the digital information to be transmitted;
the system anomaly detection module is used for monitoring and analyzing the encrypted digital information transmission in real time;
the system analysis monitoring module is used for decoding the network port and the specific IP address, converting the decoding, and protecting the data interface meeting intrusion detection in real time;
the system response detection module comprises an active monitoring module and a passive detection module; the active monitoring module is used for information automatic attack and active defense; timely defending according to the setting of the system by the user; the passive monitoring module feeds generated network attack information back to the user according to the analysis of the big data; the active scanning detection module adopts a circulating data filtering mode to continuously monitor and patrol system holes for 24 hours.
4. The method for securely transmitting the digital information of the internet of things according to claim 1, wherein the method comprises the following steps: the data decryption module obtains a plurality of key verification original digital information through a network sharing port, compares the plurality of key output data information with standard data information set by a cloud service platform, and the comparison method comprises the following steps:
step (1): the formula for determining the characteristic parameters of the key is as follows:
in the formula (1),G h represent the firsthEncryption rule parameters of the individual keys;Z h represent the firsthCharacter length of the individual keys;x hg represent the firsthFirst of the individual keysgCoefficients corresponding to the encryption elements of the individual characters;h=1，2，3，…，k，krepresenting the total number of keys;g=1，2，3，…，n，nthe total number of characters representing the key;
step (2): the cloud service platform determines the formula of the authentication parameter as follows:
in the formula (2),r f represent the firstfContent characteristics of the individual authentication rules;b f represent the firstfAuthentication weights of the authentication rules;qformat authentication parameters representing a cloud service center;F f represent the firstfMatching parameters of the authentication rules;
representing length authentication parameters of the cloud service platform;f=1，2，3，…，k；Crepresenting platform parameters set by a user; m represents the total number of authentication rules; comparing and authenticating the acquired key characteristic parameters through the authentication parameters of the cloud service platform, and if the key characteristic parameters are the same as the authentication parameters of the cloud service platform, not changing the key; if the key characteristic parameter is different from the authentication parameter of the cloud service platform, the key is modified and the replaced key is stolen by a third party in the transmission process.
5. The method for securely transmitting the digital information of the internet of things according to claim 1, wherein the method comprises the following steps: the single LSTM block adopts a memory neural network algorithm model to realize fault diagnosis, wherein the working method of the memory neural network algorithm model comprises the following steps:
the digital information is input, deleted and read, so that the digital information processing is realized, the updating of a digital information base is continuously realized, and the screening capability of the digital information is improved; set C t Storing information for the digital information; f (f) t Removing information i from digital information t Digital information inflow information, O t Streaming information of the digital information;
extracting digital information characteristics output by a single LSTM block by adopting a Sigmoid function as the input of a fault detection model, and obtaining a model predicted value;
calculating a monolithic LSTM block output function, wherein the function formula is as follows:
in the formula (3), t represents different network node parameter data nodes in the neural network model, W [ i, f, C, O ] represents parameter weight matrixes in the neural network model, b [ i, f, C, O ] represents bias vectors of different node weight matrixes in the neural network model, X represents input digital information operation parameters, and Y represents digital information operation fault diagnosis data output parameters; k represents the digital information inflow speed; m represents abnormal information in the digital information; w represents the digital information outflow speed;
Reading a single LSTM block to output storage information, wherein the output function is as follows:
in the formula (4), tanh is expressed as a hyperbolic tangent function operation,
representing a multiplication function calculated according to the elements in the neural network node; and calculating an absolute value by taking a difference between the predicted value of the memory neural network model and the output storage information of the single LSTM block, judging whether the fault occurs, if the difference exists, the fault occurs, and if the difference does not exist, the fault does not occur.
6. The method for securely transmitting the digital information of the internet of things according to claim 1, wherein the method comprises the following steps: the working state of the memory improves the memory capacity by setting a timing trigger mode and a interception trigger mode; the timing triggering mode switches the memory to a working state according to the time interval, and stores the original digital information into the memory; the interception trigger mode is used for intercepting the state of the transmission channel in real time; and if the transmission channel has data to be received, switching the memory to a working state, and storing the decrypted original data into the memory.
7. The method for securely transmitting the digital information of the internet of things according to claim 1, wherein the method comprises the following steps: the speed accelerator comprises an input buffer zone module, an AI operator acceleration module and an output buffer zone; the input buffer zone module is used for receiving and buffering the digital information, reducing the interrupt frequency of the CPU and relaxing the corresponding time limit on the CPU interrupt; the AI operator acceleration module is used for accelerating the received digital information, and carrying out quick encryption processing on the received digital information through logic judgment, network calculation and network update of the AI operator; the output buffer area is used for outputting the encrypted digital information and relieving speed mismatch between the CPU and the IO equipment.
8. The method for securely transmitting the digital information of the internet of things according to claim 1, wherein the method comprises the following steps: the information encoder comprises an information receiving module, a Bayesian classification module, an information mapping module and an information output module; the information receiving module is used for receiving the transmitted digital information; the Bayesian classification module is used for training and classifying the received digital information; the information mapping module is used for carrying out information coding on the training classified digital information according to coding rules; the information output module is used for outputting the coded digital information; the output end of the information receiving module is connected with the input end of the Bayesian classification module, the output end of the Bayesian classification module is connected with the input end of the information mapping module, and the output end of the information mapping module is connected with the input end of the information output module.
9. The method for securely transmitting the digital information of the internet of things according to claim 1, wherein the method comprises the following steps: the working method of the game training algorithm GTA module is as follows:
the maximum safe operation threshold value of the current monitoring point is set as follows:
in the formula (5), the amino acid sequence of the compound,
maximum safe operation threshold value representing current monitoring data interaction node,/->
Representing the operation rule of the encrypted data,/- >
Representing predicted risk value of monitoring point,/->
Encryption data operation parameter of transmission data interaction node of Internet of things>
Indicating environmental influence factors to which data nodes are transmitted by the Internet of things (IOT)>
Represented is node argument, ++>
Representing the operating parameter argument, +.>
Representing environmental factor arguments;
and (3) carrying out game induction on node data by using a GTA algorithm, and recording a target function as shown in a formula (6):
in the formula (6), the amino acid sequence of the compound,
an objective function representing the input of the GTA algorithm, +.>
Representing a gaming function corresponding to pick node data, +.>
Representing the operation of a normal case node gaming function, +.>
Representing communication node parameters in the database, +.>
Representing the change of the device parameters in the game state;
the GTA algorithm reflects the game relation among individuals to a certain extent, and the game mode is shown as a formula (7):
in the formula (7), the amino acid sequence of the compound,
representing the designed individual game mode of the GTA algorithm, < ->
Representing the individual nodes' secure operational data,
representing node gaming restriction functions,/->
Representing constraint argument->
Representing the normal function of node gaming, +.>
Representing the operation standard of the transmission data interaction node of the Internet of things;
inputting the data of each node in the transmission data interaction node of the Internet of things into an algorithm program in a GTA algorithm game mode to obtain a final operation result, and then, assuming in the algorithm operation process 
Equation (8) can be obtained:
in the formula (8), the amino acid sequence of the compound,
indicating the result of the modified game after the GTA algorithm is operated, < + >>
Representing the stability prediction value after the node game,
indicating an irresistible factor in the game process, < +.>
Indicating that each node of the data transmission interaction node of the Internet of things changes by electric energy,/-for example>
Representing different game group numbers, +.>
Representing the operation of a normal case node gaming function, +.>
Representing game coefficients +.>
The influence factor coefficients are shown;
finally, the method adjusts the reasons for the risk loopholes generated by different communication nodes, and the adjustment scheme is shown as a formula (9):
in the formula (9), the amino acid sequence of the compound,
indicating the optimal adjustment scheme deduced by the GTA algorithm, < + >>
Represents the maximum stable adjustment amount achieved by the adjustment function, < ->
Representing the actual stable adjustment amount, +.>
Representing the node stability variation function, +.>
Representing the outcome function of the game,
representing the coefficient of variation variable, ">
And the adjusted game result after the GTA algorithm is operated is shown.
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