CN115619555B - Electronic commerce transaction system with data encryption transmission function - Google Patents
Electronic commerce transaction system with data encryption transmission function Download PDFInfo
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Abstract
The electronic commerce transaction system comprises an intelligent parameter setting module, a blockchain module, an encryption module, an electronic commerce transaction evaluation module and a transaction module, wherein the intelligent parameter setting module is used for setting the quantitative requirement of a user, the blockchain module is used for safely storing, updating and recording data and transaction activities, the encryption module adopts a symmetric block encryption algorithm based on a neural network chaotic sequence and an asymmetric encryption algorithm based on a neural network chaotic attractor to carry out secondary encryption on the data, the electronic commerce transaction evaluation module selects a support vector machine, a proper electronic commerce transaction type is selected for the user, and the transaction is completed in the transaction module. The invention has the beneficial effects that: the method can effectively prevent the leakage of personal information and transaction information of the clients and ensure the electronic commerce transaction of the clients.
Description
Technical Field
The invention relates to the field of electronic commerce, in particular to an electronic commerce transaction system for data encryption transmission.
Background
With the development of internet technology, network space has become the basis for survival and development of people in modern society, however, due to the unsafe nature of the internet, various information security problems exist, and network attacks include disguise, fraud, eavesdropping, illegal access, falsification, denial of service, virus propagation, and the like. The blockchain is a new data structure, has dispersity and no need of trust, is owned, managed and supervised by all nodes in a network, does not accept unilateral control, and is used as a novel financial mode of commercial banks nowadays, mainly takes a core enterprise in the electronic commerce as an entry point, provides related financial products and electronic commerce services through the contact of a plurality of enterprises in the electronic commerce, and can improve the business structure of the commercial banks to a certain extent, so that the commercial banks have stronger competitive advantages. In general, the authenticity of data depends on the trust of a system center or a third party entity, such as a master node, a central database, a system responsible person, a database manager and the like, once the system center is not trusted, the authenticity of the data is destroyed, and the data is hard to find, so that the data of an electronic commerce platform is necessary to be encrypted, the encrypted data of the digital electronic commerce platform is not concentrated by the node, a server and the database, the operation and maintenance of the system are independent of the manager, the network node strictly packages the digital fingerprint of transaction information in a specific time into blocks, and the blocks are quickly broadcasted to the whole network, and a hash technology is combined for forming a closely-linked chain among the blocks to form a highly-safe public account, namely a blockchain, so that the blockchain technology has a good effect on the data encryption, but the encrypted data is complex in the electronic commerce platform adopting the blockchain technology, and the encrypted data is easy to distort or even lose, and the security and the reliability of the data encryption of the electronic commerce platform are influenced.
Disclosure of Invention
In view of the foregoing, the present invention is directed to an electronic commerce transaction system for encrypted data transmission.
The aim of the invention is realized by the following technical scheme:
the electronic commerce transaction system is characterized by comprising an intelligent parameter setting module, a blockchain module, an encryption module, an electronic commerce transaction evaluation module and a transaction module, wherein the intelligent parameter setting module is used for quantifying the requirements of users, and comprises a transaction ID, a transaction type, a timestamp, a confidentiality level, a transaction object, a transaction address and a transaction amount; the encryption module adopts a symmetric block encryption algorithm based on a neural network chaotic sequence and an asymmetric encryption algorithm based on a neural network chaotic attractor to carry out secondary encryption on data, the electronic commerce transaction evaluation module selects a support vector machine to select a proper electronic commerce transaction type for a user, and the user completes the transaction in the transaction module.
Further, the intelligent parameter setting module is used for quantifying the requirements of the user, and comprises a transaction ID, a transaction type, a time stamp, a security level, a transaction object, a transaction address and a transaction amount, which are established in a dictionary mode and comprise keys and Key values, and the Key values are marked as { Key: value }, wherein the time stamp is uniformly numbered through a hash function after being input according to standard time, the selection range is the combination of numbers and 26 lower case letters, and the security level comprises three levels of public security, common security and special security.
Further, transaction behaviors in the blockchain are sorted through the blockchain module, data which are completely repeated are distinguished according to transaction types, the data which are completely repeated are deleted, default data need to be marked and timely supplemented, key value results of each data in each transaction type need to fall into a certain section, the section comprises a normal value section, an abnormal value section and an unreliable section, wherein the normal value section indicates that the key value stored by the user is correct, the abnormal value section indicates that the key value stored by the user is incorrect, the abnormal value is highlighted at the moment, the unreliable section indicates that the key value stored by the user is problematic, whether the result of data input needs to be rechecked for the storage of the key value of the user is correct or not is required, the transaction behaviors stored in the blockchain are modified if the result of the key value input by the user is incorrect, and the transaction behaviors of the user need to be rechecked if the result of the key value input by the user is correct.
Furthermore, the encryption module encrypts data by adopting a symmetric block encryption algorithm based on a neural network chaotic sequence, and the assumption E K () Represents encryption operation, M represents data to be encrypted, D K () And C represents the data which needs to be decrypted after encryption, and the following conditions are satisfied: e (E) K (M)＝C，D K (C) =m, and there are: d (D) K (E K (M))=m, wherein the symmetric block encryption algorithm based on the neural network chaotic sequence needs to satisfy that the plaintext to be encrypted is 56 bits, the check bit is 8 bits, and the total bit is 64 bits, and the specific steps are as follows:
(1) Firstly, carrying out initial transformation on a plaintext, dividing an information block into two parts, and then carrying out transformation on a product through a function, wherein 16 times of execution are needed;
(2) After the product transformation, the two pieces of information are combined to perform the inverse initial transformation operation, and the left-shifted message becomes 48 bits;
(3) Replacing the 32-bit new data with the final result;
(4) And (3) performing a replacement operation according to the steps (1) - (3), and completing the encryption process after 16 execution cycles.
Further, the encryption module, assuming that given a key k, if k generates a subkey k1, k2, k16, k is called a weak key, satisfying:
DC(DC(M,k),k)＝M
DC -1 (DC -1 (M,k),k)＝M
DC(M,k)＝DC -1 (M,k)
wherein DC () represents a symmetric block encryption algorithm based on a neural network chaotic sequence, DC -1 () Decryption algorithm representing symmetric blocks based on neural network chaotic sequence, if c=dc (M, k), there is c=dc (M ', k '), where M ', C ' and k ' are non-taking operations, public key is used when encrypting data, private key is used when decrypting data, and conventional symmetric encryption system encrypts and decrypts each time using the same keyPublic key cryptography uses two unrelated keys to secure the network, the data, and the key itself, expressed as follows:
E K1 (M)＝C
D K2 (C)＝M
D K2 (E K1 (M))＝M
the data of the electronic commerce platform is encrypted by a symmetric block encryption algorithm based on a chaotic sequence of a neural network, the chaotic neural network consists of chaotic neurons, external input and internal feedback input, a single chaotic neuron is provided with feedback and external input items from the internal neurons, unstable items and threshold values from the neurons, and equations of i neurons of the chaotic neural network consisting of M chaotic neurons are as follows:
wherein x is i (t+1) is the output of the ith chaotic neuron at discrete time (t+1), f i Is the continuous output function of the ith chaotic neuron, M is the number of the chaotic neurons, W i,j Is the connection weight of the jth chaotic neuron and the ith chaotic neuron, h j Is the axis mutation transfer function of the jth chaotic neuron, N is the number of external inputs, V ij Is the connection weight of the jth input and the ith chaotic neuron, I j (t-r) is the intensity, g, of the jth input at discrete time (t-r) i Is the refractory function of the ith chaotic neuron, k is the refractory decay coefficient, r is the self-feedback coefficient, and r>0，T i Is the full or non-firing threshold of the ith chaotic neuron, if y i (t+1) represents the internal state of the ith chaotic neuron at the discrete time (t+1), and the iteration of the chaotic neural network is represented as follows:
x i (t+1)＝f i (y i (t+1))
for all neurons, the functions h and g are defined as h (x) =g (x) =x, where f is a sign function, namely:
the external input intensity of each neuron at any time is set to the initial external input intensity value, namely: />
Assume that all firing thresholds for each neuron are θ, with the following values:
wherein y is i (t) and y i (t+1) is the internal state of the ith chaotic neuron at discrete times t and t+1, respectively, assuming W ij Can take the values of 1, 0 and-1, W when they are in excited state ij = -1, when they are in the inhibited state, then W ij When they are not directly connected, w=1 ij =0, equalizes the number of excitation and suppression connections based on the statistical properties of the neural network and increases the unpredictability of the neural network output sequence, since the chaotic neural network is introduced on the basis of the Hopfield neural network model with time lags, the requirement of constructing the connection matrix according to Hopfield, when i=j, we assume W ij =0 to obtain values of the connection weight matrix.
Further, for the selection of parameters k, r and i, the requirement is an integer, followed by y i (t+1) to be subject to non-periodic fluctuation with 0 as a center, the chaotic neural network is designed to be updated as follows:
where α is the correction factor, defining a discrete Hopfield neural network having N interconnected neurons, each neuron having a state S i (t)＝{S 0 (t),S 1 (t),…,S N-1 (t)}，S i (t) is 0 or 1, the next state S i (t+1) depends on the current state of the neuron, i.e
Wherein T is ij Is the connection weight of neurons i and j, which is a symmetric matrix, t i Is the threshold of neuron i, S i (t) is the state of the ith neuron at time t, S i (t+1) is the state of the ith neuron at the (t+1) time, S j (t) is the state of the jth neuron at time t, and the energy of the neural network at time t is as follows:
with the evolution of the system state, the energy function is monotonically reduced, and due to the limited energy of the neural network, the neural network finally reaches a stable state and is defined as an attractor, the attractor is a chaotic attractor, that is, the attractor is irrelevant to the rule between the attractor and the initial state, unpredictable relations exist between state messages in the attraction domain of each attractor, if the connection weight matrix T is changed, the attractor and the corresponding attraction domain thereof are correspondingly changed, and the original initial state S and the attractor S are obtained after the random transformation matrix H is introduced N Respectively to new initial states
And attractor->
The method comprises the following steps:
the synaptic connection matrix between neurons consists of +1, 0 and-1, with +l, 0 and-1 indicating that the two neurons are in excited state, no direct connection and inhibited state, respectively, and based on statistical probability, if there are more unpredictable attractions, the number of excitatory synaptic connections in the network is equal to the number of inhibitory synaptic connections, assuming that the number of samples stored in the network is 8 and the convergence domain element is 20, the connected synaptic matrix is derived from the following equation:
in order to guarantee the encryption effect, it is necessary to perform security analysis on the symmetric packet encryption algorithm based on the neural network chaotic sequence, assuming that it is converted into a binary sequence C, which is derived from the following equation:
C＝C{C(i)＝2c(i)-1},1≤i≤m
wherein C (i) ∈ {0,1}, C (i) ∈ { -1,1}, and the binary autocorrelation function R is as follows:
the cross-correlation function of sequences x and y is given by:
on the basis, the design of a symmetrical package encryption algorithm based on a neural network chaotic sequence and an asymmetrical package encryption algorithm based on a neural network chaotic attractor is analyzed, and the method has safety in electronic commerce transaction transmission.
Furthermore, the e-commerce transaction evaluation module optimizes kernel function parameters and penalty factors of the support vector machine by adopting a particle swarm algorithm.
Further, the particle swarm algorithm is set to update the (t+1) moment in the search space by adopting the following steps:
step (1): and carrying out similar update detection on each particle in the particle swarm: let z i Represents the ith particle, z, in the particle group i Represents the first particle in the particle group, when particle z i And particle z i At time t, the following conditions are satisfied: i X i (t)-X l (t) is less than or equal to L (t) and is equal to |Prest i (t)-Pbest l When (t) | is less than or equal to L (t), judging the particles z i And particle z l At time t is a similar update particle, where X i (t) represents the particle z at time t i At the location of the search space, X l (t) represents the particle z at time t l At the location of the search space, prest i (t) represents the particle z at time t i At the individual optimal location of the search space, pbest l (t) represents the particle z at time t l At the individual optimal position of the search space, L (t) is the similarity detection threshold value of the particle swarm at the moment t, and
wherein L is i (t) represents the particle z at time t i Neighborhood similarity in search space, and +.>
Wherein (1)>
Indicating the distance position X in the particle swarm at the time t i (t) the position of the particles of the a-th nearest particle, c is a given positive integer, and c<N, N is the total number of particles in the particle swarm;
the particles which are judged to be similar updated particles in the particle swarm are classified, specifically: set S k (t) represents a kth class, class S, obtained by classifying particles in the particle group and similar updated particles thereof at time t k The particles of (t) are selected from the population of particles in the following manner: randomly selecting one particle from the uncategorized particles of the current particle swarm to be added into the class S k In (t), when the randomly selected particles do not have similar updated particles in the population,stopping selecting particles in the population to add to class S k (t) when the randomly selected particles have similar updated particles in the population of particles, adding the similar updated particles of the randomly selected particles to class S k In (t), and continuing to neutralize the current class S in the population of particles k Adding S to the particles of which any one particle is a similar updated particle k In (t) up to class S k When the particles in (t) do not have similar updated particles in the current population, stopping selecting particles in the population and adding the particles into the class S k (t);
let S (t) denote a class set obtained by classifying particles in a particle group and similar updated particles thereof at time t, and S (t) = { S k (t), k=1, 2, …, M (t) }, where M (t) represents the number of classes in the set S (t);
step (2): the particles in each class in the set S (t) are updated at time (t+1) in the following manner: let N be k (t) represents class S k The number of particles in (t),
for a given positive integer, for determining the update pattern of particles in the class of set S (t), and +.>
Class S k The particles in (t) satisfy: />
When it is, the following method is adopted for class S k In (t), the particles are updated at the time (t+1):
V k,j (t+1)＝ω(t)V k,j (t)+c 1 rand 1 (Pbest k,j (t)-X k,j (t))+c 2 rand 2 (Gbest(t)-Xk ,j (t))
X k,j (t+1)＝X k,j (t)+V k,j (t+1)
in the above updated formula, let z k,j Representation class S k The jth particle, X, in (t) k,j (t+1) and V k,j (t+1) represents the particles at the time (t+1), respectivelyz k,j Position and step size in search space, X k,j (t) and V k,j (t) each represents a particle z at time t k,j In the position and step size of the search space, ω (t) represents the inertial weight factor of the particle swarm at time t, an
ω max And omega min Respectively a given maximum inertial weight factor and minimum inertial weight factor, and ω max ＝0.9，ω min ＝0.4，T max Represents the maximum iteration number, rand 1 And rand 2 Respectively in the interval [0,1 ]]Internally generated random numbers, pbest k,j (t) represents the particle z at time t k,j In the individual optimal position of the search space, gbest (t) represents the global optimal position of the particle swarm in the search space at the moment t, c 1 A local learning factor representing a population of particles c 2 Global learning factor representing particle swarm, c 1 And c 2 The value of (2) may be: c 1 ＝2，c 2 ＝2；
Class S k The particles in (t) satisfy:
when it is, the following method is adopted for class S k In (t), the particles are updated at the time (t+1):
V k,j (t+1)＝ω k,j (t)V k,j (t)+c 1 rand 1 (Pbest k,j (t)-X k,j (t))+c 2 rand 2 (gbest(t)-X k,j (t))
X k,j (t+1)＝X k,j (t)+V k,j (t+1)
in the above updated formula ω k,j (t) represents the particle z at time t k,j Inertial weighting factor in search space, ω k,j The value of (t) is set as:
wherein ρ is k (t) represents class S k Historical similarity coefficients of particles in (t), an
Wherein z is k,b Representation class S k The b-th particle, X in (t) k,b (t-1) represents the time particle z at (t-1) k,b At the location of the search space, X k,j (t-1) represents the time particle z at (t-1) k,j At the location of the search space, rand k,j (t) is expressed in interval->
A random number generated therein.
The invention has the beneficial effects that: the encryption module adopts a symmetric block encryption algorithm based on a neural network chaotic sequence and an asymmetric encryption algorithm based on a neural network chaotic attractor to encrypt and decrypt data secondarily, so that noise in the data is small after the electronic commerce data is encrypted and decrypted, the accuracy of the data encryption and decryption is ensured, the loss and distortion influence of the encrypted data are reduced, and the accuracy of the encrypted and decrypted data is ensured; the method comprises the steps that an electronic commerce transaction evaluation module optimizes parameters of a support vector machine by adopting an optimized particle swarm algorithm, firstly, similar update detection is carried out on particles in a particle swarm, the particles with similar update modes are classified, when the particles are updated, when the number of the particles with similar update modes in the class where the particles are located is small, the particles in the class are set to continuously update according to the update modes of a standard particle swarm algorithm, so that the advantages of the standard particle swarm algorithm in optimizing are maintained, when the number of the particles with similar update modes in the class where the particles are located is large, in order to ensure the diversity after the updating of the particle swarm, the similarity of the previous update step length of the particles in the class is judged by detecting the similarity of the previous positions of the particles in the class, when the previous positions of the particles in the class have large difference, the step length of the particles in the class where the previous update is indicated is large, at the moment, the inertia weight factors of the particles in the class are enhanced in the updating process, so that the diversity of the updated positions of the particles in the class is increased, on the contrary, when the previous positions of the particles in the class have large similarity, the similarity of the particles in the class where the particle swarm is located, the similarity of the particle swarm is increased, the similarity of the particle in the class is increased, and the current transaction evaluation algorithm is based on the similarity after the current, and the accuracy of the similarity is improved, and the accuracy of the particle swarm optimization is improved.
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The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation on the invention, and other drawings can be obtained by one of ordinary skill in the art without undue effort from the following drawings.
Fig. 1 is a schematic diagram of the structure of the present invention.
Detailed Description
The invention will be further described with reference to the following examples.
Referring to fig. 1, an electronic commerce transaction system for encrypted data transmission of the present embodiment is characterized by comprising an intelligent parameter setting module, a blockchain module, an encryption module, an electronic commerce transaction evaluation module and a transaction module, wherein the intelligent parameter setting module is used for quantifying the requirements of a user, and comprises a transaction ID, a transaction type, a timestamp, a security level, a transaction object, a transaction address and a transaction amount, the requirements are established in a dictionary mode, and the requirements of the user after quantification are stored in the blockchain module, the blockchain module is used for safely storing, updating, recording data and transaction activities, and sorting electronic commerce transaction behaviors recorded in the blockchain, grouping the data according to the transaction type, deleting completely repeated data, labeling and timely supplementing default data, detecting whether a key value result of each data in each group falls into a certain interval, wherein the interval comprises a normal value interval, an abnormal value interval and an unreliable interval, at this time, the abnormal value interval represents that the key value stored by the user is correct, the key value stored by the user is not, the abnormal value interval represents that the key value stored by the user is not correct, if the key value stored by the user is not correct, the key value is not correct, and if the key value is not correct and the correct transaction value is required to be input by the user, and the correct key value is not correct; the encryption module adopts a symmetric block encryption algorithm based on a neural network chaotic sequence and an asymmetric encryption algorithm based on a neural network chaotic attractor to carry out secondary encryption on data, the electronic commerce transaction evaluation module selects a support vector machine to select a proper electronic commerce transaction type for a user, and the user completes the transaction in the transaction module.
Specifically, the intelligent parameter setting module is used for quantifying the requirements of a user, and comprises a transaction ID, a transaction type, a timestamp, a security level, a transaction object, a transaction address and a transaction amount, which are established in a dictionary mode and comprise keys and Key values, and the Key values are marked as { Key: value }, wherein the timestamp is uniformly numbered through a hash function after being input according to standard time, the selection range is the combination of numbers and 26 lower case letters, and the security level comprises three levels of public security, common security and special security.
Specifically, transaction behaviors in a blockchain are sorted through a blockchain module, data are distinguished according to transaction types, data which are completely repeated are deleted, default data need to be marked and timely supplemented, key value results of each data in each transaction type need to fall into a certain section, the section comprises a normal value section, an abnormal value section and an unreliable section, wherein the normal value section indicates that key values stored by a user are correct, the abnormal value section indicates that key values stored by the user are incorrect, at the moment, the abnormal value is highlighted, the unreliable section indicates that key values stored by the user are problematic, whether the data input result is correct or not needs to be rechecked for the key value storage of the user, if the key value input result is incorrect by the user, the transaction behaviors stored in the blockchain are modified, and if the key value input result is correct by the user, the transaction behaviors of the user need to be rechecked.
Specifically, the encryption module encrypts data by adopting a symmetric block encryption algorithm based on a neural network chaotic sequence, and the assumption E K () Represents encryption operation, M represents data to be encrypted, D K () And C represents the data which needs to be decrypted after encryption, and the following conditions are satisfied: e (E) K (M)＝C，D K (C) =m, and there are: d (D) K (E K (M))=m, wherein the symmetric block encryption algorithm based on the neural network chaotic sequence needs to satisfy that the plaintext to be encrypted is 56 bits, the check bit is 8 bits, and the total bit is 64 bits, and the specific steps are as follows:
(1) Firstly, carrying out initial transformation on a plaintext, dividing an information block into two parts, and then carrying out transformation on a product through a function, wherein 16 times of execution are needed;
(2) After the product transformation, the two pieces of information are combined to perform the inverse initial transformation operation, and the left-shifted message becomes 48 bits;
(3) Replacing the 32-bit new data with the final result;
(4) And (3) performing a replacement operation according to the steps (1) - (3), and completing the encryption process after 16 execution cycles.
Preferably, the encryption module, given a key k, if k generates a subkey k1, k2, k16, k is called a weak key, satisfying:
DC(DC(M,k),k)＝M
DC -1 (DC -1 (M,k),k)＝M
DC(M,k)＝DC -1 (q,k)
wherein DC () represents a symmetric block encryption algorithm based on a neural network chaotic sequence, DC -1 () Decryption algorithm representing symmetric blocks based on neural network chaotic sequence, if c=dc (M, k), there is c=dc (M ', k '), where M ', C ' and k ' are non-taking operations, public key is used when encrypting data, private key is used when decrypting data, traditional symmetric encryption system encrypts and decrypts each time using the same key, public key encryption system uses two uncorrelated keys to ensure network security, data security and key security, tableThe expression is as follows:
E K1 (M)＝C
D K2 (C)＝M
D K2 (E K1 (M))＝M
the data of the electronic commerce platform is encrypted by a symmetric block encryption algorithm based on a chaotic sequence of a neural network, the chaotic neural network consists of chaotic neurons, external input and internal feedback input, a single chaotic neuron is provided with feedback and external input items from the internal neurons, unstable items and threshold values from the neurons, and equations of i neurons of the chaotic neural network consisting of M chaotic neurons are as follows:
wherein x is i (t+1) is the output of the ith chaotic neuron at discrete time (t+1), f i Is the continuous output function of the ith chaotic neuron, M is the number of the chaotic neurons, W i,j Is the connection weight of the jth chaotic neuron and the ith chaotic neuron, h j Is the axis mutation transfer function of the jth chaotic neuron, N is the number of external inputs, V ij Is the connection weight of the jth input and the ith chaotic neuron, I j (t-r) is the intensity, g, of the jth input at discrete time (t-r) i Is the refractory function of the ith chaotic neuron, k is the refractory decay coefficient, r is the self-feedback coefficient, and r>0，T i Is the full or non-firing threshold of the ith chaotic neuron, if y i (t+1) represents the internal state of the ith chaotic neuron at the discrete time (t+1), and the iteration of the chaotic neural network is represented as follows:
x i (t+1)＝f i (y i (t+1))
for all neurons, the functions h and g are defined ash (x) =g (x) =x, where f is a sign function, i.e.:
the external input intensity of each neuron at any time is set to the initial external input intensity value, namely: />
Assume that all firing thresholds for each neuron are θ, with the following values:
wherein y is i (t) and y i (t+1) is the internal state of the ith chaotic neuron at discrete times t and t+1, respectively, assuming W ij Can take the values of 1, 0 and-1, W when they are in excited state ij = -1, when they are in the inhibited state, then W ij When they are not directly connected, w=1 ij =0, equalizes the number of excitation and suppression connections based on the statistical properties of the neural network and increases the unpredictability of the neural network output sequence, since the chaotic neural network is introduced on the basis of the Hopfield neural network model with time lags, the requirement of constructing the connection matrix according to Hopfield, when i=j, we assume W ij =0 to obtain the value of the connection weight matrix, which is given by the following equation for the case of m=8:
specifically, for the selection of parameters k, r and i, the requirement is an integer, followed by y i (t+1) to be subject to non-periodic fluctuation with 0 as a center, the chaotic neural network is designed to be updated as follows:
wherein alpha is a correction coefficient, defining a correction coefficient having N mutual valuesDiscrete Hopfield neural network with neurons, each neuron having a state of S i (t)＝{S 0 (t),S 1 (t),…,S N-1 (t)}，S i (t) is 0 or 1, the next state S i (t+1) depends on the current state of the neuron, i.e
Wherein T is ij Is the connection weight of neurons i and j, which is a symmetric matrix, t i Is the threshold of neuron i, S i (t) is the state of the ith neuron at time t, S i (t+1) is the state of the ith neuron at the (t+1) time, S j (t) is the state of the jth neuron at time t, and the energy of the neural network at time t is as follows:
with the evolution of the system state, the energy function is monotonically reduced, and due to the limited energy of the neural network, the neural network finally reaches a stable state and is defined as an attractor, the attractor is a chaotic attractor, that is, the attractor is irrelevant to the rule between the attractor and the initial state, unpredictable relations exist between state messages in the attraction domain of each attractor, if the connection weight matrix T is changed, the attractor and the corresponding attraction domain thereof are correspondingly changed, and the original initial state S and the attractor S are obtained after the random transformation matrix H is introduced N Respectively to new initial states
And attractor->
The method comprises the following steps:
the synaptic connection matrix between neurons consists of +1, 0 and-1, with +l, 0 and-1 indicating that the two neurons are in excited state, no direct connection and inhibited state, respectively, and based on statistical probability, if there are more unpredictable attractions, the number of excitatory synaptic connections in the network is equal to the number of inhibitory synaptic connections, assuming that the number of samples stored in the network is 8 and the convergence domain element is 20, the connected synaptic matrix is derived from the following equation:
in order to guarantee the encryption effect, it is necessary to perform security analysis on the symmetric packet encryption algorithm based on the neural network chaotic sequence, assuming that it is converted into a binary sequence C, which is derived from the following equation:
C＝C{C(i)＝2c(i)-1},1≤i≤m
wherein C (i) ∈ {0,1}, C (i) ∈ { -1,1}, and the binary autocorrelation function R is as follows:
the cross-correlation function of sequences x and y is given by:
on the basis, the design of a symmetrical package encryption algorithm based on a neural network chaotic sequence and an asymmetrical package encryption algorithm based on a neural network chaotic attractor is analyzed, and the method has safety in electronic commerce transaction transmission.
Furthermore, the e-commerce transaction evaluation module optimizes kernel function parameters and penalty factors of the support vector machine by adopting a particle swarm algorithm.
Further, the particle swarm algorithm is set to update the (t+1) moment in the search space by adopting the following steps:
step (1): and carrying out similar update detection on each particle in the particle swarm: let z i Represents the ith particle, z, in the particle group i Represents the first particle in the particle group, when particle z i And particle z i At time t, the following conditions are satisfied: i X i (t)-X l (t) is less than or equal to L (t) and is equal to |Prest i (t)-Pbest l When (t) | is less than or equal to L (t), judging the particles z i And particle z l At time t is a similar update particle, where X i (t) represents the particle z at time t i At the location of the search space, X l (t) represents the particle z at time t l At the location of the search space, prest i (t) represents the particle z at time t i At the individual optimal location of the search space, pbest l (t) represents the particle z at time t l At the individual optimal position of the search space, L (t) is the similarity detection threshold value of the particle swarm at the moment t, and
wherein L is i (t) represents the particle z at time t i Neighborhood similarity in search space, and +.>
Wherein (1)>
Indicating the distance position X in the particle swarm at the time t i (t) the position of the particles of the a-th nearest particle, c is a given positive integer, and c<N, N is the total number of particles in the particle swarm;
the particles which are judged to be similar updated particles in the particle swarm are classified, specifically: set S k (t) represents a kth class, class S, obtained by classifying particles in the particle group and similar updated particles thereof at time t k The particles of (t) are selected from the population of particles in the following manner: among the uncategorized particles of the current population of particlesRandomly selecting a particle to add to class S k (t) when the randomly selected particles do not have similar updated particles in the population, stopping selecting particles in the population to add to the class S k (t) when the randomly selected particles have similar updated particles in the population of particles, adding the similar updated particles of the randomly selected particles to class S k In (t), and continuing to neutralize the current class S in the population of particles k Adding S to the particles of which any one particle is a similar updated particle k In (t) up to class S k When the particles in (t) do not have similar updated particles in the current population, stopping selecting particles in the population and adding the particles into the class S k (t);
let S (t) denote a class set obtained by classifying particles in a particle group and similar updated particles thereof at time t, and S (t) = { S k (t), k=1, 2, …, M (t) }, where M (t) represents the number of classes in the set S (t);
step (2): the particles in each class in the set S (t) are updated at time (t+1) in the following manner: let N be k (t) represents class S k The number of particles in (t),
for a given positive integer, for determining the update pattern of particles in the class of set S (t), and +.>
Class S k The particles in (t) satisfy: />
When it is, the following method is adopted for class S k In (t), the particles are updated at the time (t+1):
V k,j (t+1)＝ω(t)V k,j (t)+c 1 rand 1 (Pbest k,j (t)-X k,j (t))+c 2 rand 2 (Gbest(t)-X k,j (t))
X k,j (t+1)＝X k,j (t)+V k,j (t+1)
in the above updated formula, let z k,j Representation class S k The jth particle, X, in (t) k,j (t+1) and V k,j (t+1) represents the particles z at the time (t+1), respectively k,j Position and step size in search space, X k,j (t) and V k,j (t) each represents a particle z at time t k,j In the position and step size of the search space, ω (t) represents the inertial weight factor of the particle swarm at time t, an
ω max And omega min Respectively a given maximum inertial weight factor and minimum inertial weight factor, and ω max ＝0.9，ω min ＝0.4，T max Represents the maximum iteration number, rand 1 And rand 2 Respectively in the interval [0,1 ]]Internally generated random numbers, pbest k,j (t) represents the particle z at time t k,j In the individual optimal position of the search space, gbest (t) represents the global optimal position of the particle swarm in the search space at the moment t, c 1 A local learning factor representing a population of particles c 2 Global learning factor representing particle swarm, c 1 And c 2 The value of (2) may be: c 1 ＝2，c 2 ＝2；
Class S k The particles in (t) satisfy:
when it is, the following method is adopted for class S k In (t), the particles are updated at the time (t+1):
V k,j (t+1)＝ω k,j (t)V k,j (t)+c 1 rand 1 (Pbest k,j (t)-X k,j (t))+c 2 rand 2 (gbest(t)-X k,j (t))
X k,j (t+1)＝X k,j (t)+V k,j (t+1)
in the above updated formula ω k,j (t) represents the particle z at time t k,j Inertial weighting factor in search space, ω k,j The value of (t) is set as:
wherein ρ is k (t) represents class S k Historical similarity coefficients of particles in (t), an
Wherein z is k,b Representation class S k The b-th particle, X in (t) k,b (t-1) represents the time particle z at (t-1) k,b At the location of the search space, X k,j (t-1) represents the time particle z at (t-1) k,j At the location of the search space, rand k,j (t) is expressed in interval->
A random number generated therein.
The invention has the beneficial effects that: the encryption module adopts a symmetric block encryption algorithm based on a neural network chaotic sequence and an asymmetric encryption algorithm based on a neural network chaotic attractor to encrypt and decrypt data secondarily, so that noise in the data is small after the electronic commerce data is encrypted and decrypted, the accuracy of the data encryption and decryption is ensured, the loss and distortion influence of the encrypted data are reduced, and the accuracy of the encrypted and decrypted data is ensured; the method comprises the steps that an electronic commerce transaction evaluation module optimizes parameters of a support vector machine by adopting an optimized particle swarm algorithm, firstly, similar update detection is carried out on particles in a particle swarm, the particles with similar update modes are classified, when the particles are updated, when the number of the particles with similar update modes in the class where the particles are located is small, the particles in the class are set to continuously update according to the update modes of a standard particle swarm algorithm, so that the advantages of the standard particle swarm algorithm in optimizing are maintained, when the number of the particles with similar update modes in the class where the particles are located is large, in order to ensure the diversity after the updating of the particle swarm, the similarity of the previous update step length of the particles in the class is judged by detecting the similarity of the previous positions of the particles in the class, when the previous positions of the particles in the class have large difference, the step length of the particles in the class where the previous update is indicated is large, at the moment, the inertia weight factors of the particles in the class are enhanced in the updating process, so that the diversity of the updated positions of the particles in the class is increased, on the contrary, when the previous positions of the particles in the class have large similarity, the similarity of the particles in the class where the particle swarm is located, the similarity of the particle swarm is increased, the similarity of the particle in the class is increased, and the current transaction evaluation algorithm is based on the similarity after the current, and the accuracy of the similarity is improved, and the accuracy of the particle swarm optimization is improved.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the scope of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (4)
1. The electronic commerce transaction system is characterized by comprising an intelligent parameter setting module, a blockchain module, an encryption module, an electronic commerce transaction evaluation module and a transaction module, wherein the intelligent parameter setting module is used for quantifying the requirements of users, and comprises a transaction ID, a transaction type, a timestamp, a confidentiality level, a transaction object, a transaction address and a transaction amount; the encryption module adopts a symmetric block encryption algorithm based on a neural network chaotic sequence and an asymmetric encryption algorithm based on a neural network chaotic attractor to carry out secondary encryption on data, the electronic commerce transaction evaluation module selects a support vector machine to select a proper electronic commerce transaction type for a user, and the user completes the transaction in the transaction module;
the intelligent parameter setting module is used for quantifying the requirements of a user, and comprises a transaction ID, a transaction type, a time stamp, a confidentiality level, a transaction object, a transaction address and a transaction amount, which are established in a dictionary mode and comprise keys and Key values, which are marked as { Key: value }, wherein the time stamp is uniformly numbered through a hash function after being input according to standard time, the selection range is the combination of numbers and 26 lower case letters, and the confidentiality level comprises three levels of public confidentiality, common confidentiality and special confidentiality;
the encryption module adopts a symmetric block encryption algorithm based on a neural network chaotic sequence to encrypt data, and the assumption E K () Represents encryption operation, M represents data to be encrypted, D K () And C represents the data which needs to be decrypted after encryption, and the following conditions are satisfied: e (E) K (M)＝C，D K (C) =m, and there are: d (D) K (E K (M))=m, wherein the symmetric block encryption algorithm based on the neural network chaotic sequence needs to satisfy that the plaintext to be encrypted is 56 bits, the check bit is 8 bits, and the total bit is 64 bits, and the specific steps are as follows:
(1) Firstly, carrying out initial transformation on a plaintext, dividing an information block into two parts, and then carrying out transformation on a product through a function, wherein 16 times of execution are needed;
(2) After the product transformation, the two pieces of information are combined to perform the inverse initial transformation operation, and the left-shifted message becomes 48 bits;
(3) Replacing the 32-bit new data with the final result;
(4) And (3) performing a replacement operation according to the steps (1) - (3), and completing the encryption process after 16 execution cycles.
2. The electronic commerce transaction system of claim 1 wherein the electronic commerce transaction evaluation module optimizes the kernel parameters and penalty factors of the support vector machine using a particle swarm algorithm.
3. The electronic commerce transaction system of claim 2, wherein the encryption module, assuming a given key k, if k generates sub-keys k1, k2, …, k16, then k is called a weak key, satisfying:
DC(DC(M,k),k)＝M
DC -1 (DC -1 (M,k),k)＝M
DC(M,k)＝DC -1 (M,k)
wherein DC () represents a symmetric block encryption algorithm based on a neural network chaotic sequence, DC -1 () A decryption algorithm representing a symmetric block based on a neural network chaotic sequence, if c=dc (M, k), there is c=dc (M ', k '), where M ', C ' and k ' are non-taking operations, a public key is used when encrypting data, and a private key is used when decrypting data, a conventional symmetric encryption system encrypts and decrypts each time using the same key, and a public key encryption system uses two uncorrelated keys to ensure network security, data security, and key security itself, expressed as follows:
E K1 (M)＝C
D K2 (C)＝M
D K2 (E K1 (M))＝M
the data of the electronic commerce platform is encrypted by a symmetric block encryption algorithm based on a chaotic sequence of a neural network, the chaotic neural network consists of chaotic neurons, external input and internal feedback input, a single chaotic neuron is provided with feedback and external input items from the internal neurons, unstable items and threshold values from the neurons, and equations of i neurons of the chaotic neural network consisting of M chaotic neurons are as follows:
wherein x is i (t+1) is the output of the ith chaotic neuron at discrete time (t+1), f i Is the continuous output function of the ith chaotic neuron, M is the number of the chaotic neurons, W i,j Is the connection weight of the jth chaotic neuron and the ith chaotic neuron, h j Is the axis mutation transfer function of the jth chaotic neuron, N is the number of external inputs, V ij Is the connection weight of the jth input and the ith chaotic neuron, I j (t-r) is the intensity, g, of the jth input at discrete time (t-r) i Is the refractory function of the ith chaotic neuron, k is the refractory decay coefficient, r is the self-feedback coefficient, and r>0，T i Is the full or non-firing threshold of the ith chaotic neuron, if y i (t+1) represents the internal state of the ith chaotic neuron at the discrete time (t+1), and the iteration of the chaotic neural network is represented as follows:
x i (t+1)＝f i (y i (t+1))
wherein t is i Representing the i-th moment, the functions h and g are defined as h (x) =g (x) =x for all neurons, where f is a sign function, namely:
the external input intensity of each neuron at any time is set to the initial external input intensity value, namely: />
Assume that all firing thresholds for each neuron are θ, with the following values: />
Wherein y is i (t) and y i (t+1) is the internal state of the ith chaotic neuron at discrete times t and t+1, respectively, assuming W ij Can take the values of 1, 0 and-1, W when they are in excited state ij = -1, when they are in the inhibited state, then W ij When they are not directly connected, w=1 ij =0, equalizes the number of excitation and suppression connections based on the statistical properties of the neural network and increases the unpredictability of the neural network output sequence, since the chaotic neural network is introduced on the basis of the Hopfield neural network model with time lags, the requirement of constructing the connection matrix according to Hopfield, when i=j, we assume W ij =0 to obtain values of the connection weight matrix. />
4. An e-commerce transaction system for encrypted transmission of data according to claim 3 wherein the parameters k, r and i are selected to be integers followed by y i (t+1) to be subject to non-periodic fluctuation with 0 as a center, the chaotic neural network is designed to be updated as follows:
where α is the correction factor, defining a discrete Hopfield neural network having N interconnected neurons, each neuron having a state S i (t)＝{S 0 (t),S 1 (t),...,S N-1 (t)}，S i (t) is 0 or 1, the next state S i (t+1) depends on the current state of the neuron, i.e
Wherein T is ij Is the connection weight of neurons i and j, which is a symmetric matrix, t i Is the threshold of neuron i, S i (t) is the state of the ith neuron at time t, S i (t+1) is the state of the ith neuron at the (t+1) time, S j (t) is the state of the jth neuron at time t, and the energy of the neural network at time t is as follows:
with the evolution of the system state, the energy function is monotonically reduced, and due to the limited energy of the neural network, the neural network finally reaches a stable state and is defined as an attractor, the attractor is a chaotic attractor, that is, the attractor is irrelevant to the rule between the attractor and the initial state, unpredictable relations exist between state messages in the attraction domain of each attractor, if the connection weight matrix T is changed, the attractor and the corresponding attraction domain thereof are correspondingly changed, and the original initial state S and the attractor S are obtained after the random transformation matrix H is introduced N Respectively to new initial states
And attractor->
The method comprises the following steps:
the synaptic connection matrix between neurons consists of +1, 0 and-1, with +l, 0 and-1 indicating that the two neurons are in excited state, no direct connection and inhibited state, respectively, and based on statistical probability, if there are more unpredictable attractions, the number of excitatory synaptic connections in the network is equal to the number of inhibitory synaptic connections, assuming that the number of samples stored in the network is 8 and the convergence domain element is 20, the connected synaptic matrix is derived from the following equation:
in order to guarantee the encryption effect, it is necessary to perform security analysis on the symmetric packet encryption algorithm based on the neural network chaotic sequence, assuming that it is converted into a binary sequence C, which is derived from the following equation:
C＝C{C(i)＝2c(i)-1},1≤i≤m
wherein C (i) ∈ {0,1}, C (i) ∈ { -1,1}, and the binary autocorrelation function R is as follows:
the cross-correlation function of sequences x and y is given by:
x (j) is the output of the ith chaotic neuron if y (i+j) represents the internal state of the (i+j) th chaotic neuron.
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