A quantum key cloud security situation awareness prediction system and method

Abstract

The application discloses a quantum key cloud security situation awareness prediction system, which comprises a data acquisition layer, a security situation assessment layer, a security situation prediction layer and a security situation early warning layer; the application also discloses a quantum key cloud security situation awareness prediction method, which is realized based on the quantum key cloud security situation awareness prediction system.The application discloses a quantum key cloud security situation awareness prediction system and method, which realizes effective evaluation and accurate prediction of the quantum key cloud security situation by combining a quantum computer, a genetic algorithm and a simulated annealing algorithm, greatly improves the stability of the quantum key cloud platform security, and provides strong protection for the protection of the increasingly huge and complex network space.

Description

Technical Field

[0001] This invention relates to the technical field of quantum secure communication, specifically to a quantum key cloud security situation awareness and prediction system and method. Background Technology

[0002] The basic principle of quantum key distribution (QKD) is based on the interdisciplinary nature of cryptography, information theory, and quantum mechanics, and it is currently a hot topic in the field of quantum cryptography research.

[0003] The development of quantum cryptography systems and the construction of quantum networks are becoming increasingly sophisticated. Quantum cryptography technology is playing an increasingly important role in commercial cryptography systems and information security. Currently, the construction of critical infrastructure centered on cloud computing has become one of the important strategies for serving national economic and social development, but it also faces increasingly serious security threats. The emergence and practical application of quantum cryptography technology has opened up a new path for ensuring the security of cloud computing infrastructure, giving rise to quantum key cloud computing.

[0004] The quantum cryptography cloud platform is of strategic significance for building a new cybersecurity defense line based on quantum secure communication. Faced with the demands of intelligent, high-speed, and complex networks, attack methods are becoming increasingly sophisticated, rendering traditional cybersecurity technologies increasingly inadequate, and making network intrusions unavoidable.

[0005] Network security situation awareness technology collects and analyzes network security data from various devices in the quantum key cloud platform to assess the current network status. This allows quantum key cloud platform security administrators to gain a more comprehensive understanding of the current network security status and predict future security trends based on past network security situations.

[0006] However, existing network security situation awareness prediction methods have relatively low prediction accuracy and slow convergence speed; moreover, the traditional neural network models used for prediction have many parameters, insufficient generalization performance, and cannot make effective predictions. Summary of the Invention

[0007] To address the issue of low prediction accuracy in existing network security situation awareness prediction methods, this invention provides a quantum key cloud security situation awareness prediction system and method.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0009] A quantum key cloud security situation awareness and prediction system includes a data acquisition layer, a security situation assessment layer, a security situation prediction layer, and a security situation early warning layer; wherein,

[0010] The data acquisition layer is used to acquire data from the quantum key cloud platform to obtain classical datasets;

[0011] The security situation assessment layer is used to combine quantum computers, genetic algorithms and simulated annealing algorithms to optimize classical datasets, obtain prediction results for classical datasets, and assess the current security situation value of the quantum key cloud platform based on the prediction results of classical datasets.

[0012] The security situation prediction layer is used to combine quantum computers, genetic algorithms and simulated annealing algorithms to optimize the current and historical security situation values ​​of the quantum key cloud platform, so as to obtain the predicted situation value of the quantum key cloud platform.

[0013] The security situation early warning layer is used to provide users with real-time early warnings about the current security status of the quantum key cloud platform based on the current and predicted security situation values.

[0014] The above scheme combines quantum computers, genetic algorithms, and simulated annealing algorithms to effectively assess and accurately predict the security situation of quantum key clouds, greatly improving the stability of quantum key cloud platform security and providing strong protection for the increasingly large and complex cyberspace.

[0015] Preferably, the data acquisition layer uses probes to acquire data from the quantum key cloud platform.

[0016] Preferably, the probes include traffic probes, log probes, asset probes, and vulnerability probes.

[0017] Preferably, the quantum computer includes an input layer, a hidden layer, and an output layer:

[0018] The input layer is used to convert the input data into quantum state information and initialize the parameter weights on the quantum circuit.

[0019] The hidden layer is used to measure quantum state information through variable quantum circuitry to obtain an output vector;

[0020] The output layer is used to map the output vector to obtain the prediction result of the input data.

[0021] Preferably, the specific steps for optimization by combining quantum computing, genetic algorithms, and simulated annealing algorithms are as follows:

[0022] A1: Randomly generate M individuals as the initial population, where each individual includes n parameter weights, and each parameter weight corresponds to a different node on the quantum circuit of the quantum computer input layer;

[0023] A2: Encode the position of the parameter weights in each individual;

[0024] A3: Calculate the fitness value of each individual based on the input and output data of the quantum computer;

[0025] A4: Determine whether the minimum fitness value is not greater than the preset minimum threshold;

[0026] If so, proceed to step A10;

[0027] If not, proceed to step A5;

[0028] A5: Determine if the current iteration count has reached the preset maximum iteration count;

[0029] If so, proceed to step A10;

[0030] If not, proceed to step A6;

[0031] A6: Sort each individual's fitness value from smallest to largest, retain the top-ranked individuals according to a preset ratio, and discard the bottom-ranked individuals;

[0032] A7: Interleave the retained individuals to create new combinations of individuals;

[0033] A8: Randomly mutate the codes of some individuals in the new individual combination;

[0034] A9: The individuals obtained from the mutation are used to form an intermediate population. After simulated annealing of the intermediate population, a new generation of population is obtained. Return to step A3 for the next round of iteration.

[0035] A10: Select the individual with the smallest fitness value as the optimal individual, and the optimization ends.

[0036] Preferably, in step A2, the positions of the parameter weights are binary encoded, with the positions of used parameter weights encoded as 1 and the positions of unused parameter weights encoded as 0.

[0037] Preferably, in step A3, the fitness value is calculated using the following fitness function:

[0038]

[0039] Where X is the data input to the quantum computer, and Y is the prediction result of X.

[0040] Preferably, it also includes a data preprocessing layer for normalizing the classic dataset input to the security situation assessment layer.

[0041] Preferably, the security situation assessment layer has a preset platform security level, and the current security situation value of the quantum key cloud platform is assessed based on the platform security level corresponding to the optimization result.

[0042] A method for predicting the security situation of a quantum key cloud, based on the aforementioned quantum key cloud security situation prediction system, includes the following steps:

[0043] S1: Collect data from the quantum key cloud platform to obtain a classical dataset;

[0044] S2: Optimize the classic dataset to obtain the prediction results for the classic dataset;

[0045] S3: Evaluate the current security posture of the quantum key cloud platform based on the prediction results of the classical dataset;

[0046] S4: Optimize the current and historical security status values ​​of the quantum key cloud platform to obtain the predicted status value of the quantum key cloud platform;

[0047] S5: Provides real-time alerts to users regarding the current security status of the quantum key cloud platform based on its current and predicted security status values.

[0048] Beneficial technical effects of the present invention:

[0049] This invention provides a quantum key cloud security situation awareness and prediction system and method. By combining quantum computers, genetic algorithms and simulated annealing algorithms, it can effectively assess and accurately predict the security situation of quantum key clouds, greatly improve the stability of quantum key cloud platform security, and provide strong protection for the increasingly large and complex cyberspace. Attached Figure Description

[0050] Figure 1 This is a structural block diagram of a quantum key cloud security situation awareness and prediction system according to the present invention;

[0051] Figure 2 This is a flowchart illustrating the optimization process combining quantum computing, genetic algorithms, and simulated annealing algorithms in this invention.

[0052] Figure 3 This is a flowchart illustrating the implementation steps of the technical solution of the present invention;

[0053] The layers are: 1. Data acquisition layer; 2. Security situation assessment layer; 3. Security situation prediction layer; 4. Security situation early warning layer; 5. Data preprocessing layer. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments. However, the scope of protection of this invention is not limited to the specific embodiments described below.

[0055] Example 1

[0056] like Figure 1 As shown, a quantum key cloud security situation awareness and prediction system includes a data acquisition layer 1, a security situation assessment layer 2, a security situation prediction layer 3, and a security situation early warning layer 4; wherein,

[0057] The data acquisition layer 1 is used to acquire data from the quantum key cloud platform to obtain a classical dataset;

[0058] The security situation assessment layer 2 is used to combine quantum computers, genetic algorithms and simulated annealing algorithms to optimize classical datasets, obtain prediction results for classical datasets, and assess the current security situation value of the quantum key cloud platform based on the prediction results of classical datasets.

[0059] The security situation prediction layer 3 is used to combine quantum computers, genetic algorithms and simulated annealing algorithms to optimize the current security situation value and historical security situation value of the quantum key cloud platform, so as to obtain the predicted situation value of the quantum key cloud platform.

[0060] The security situation warning layer 4 is used to provide users with real-time warnings about the current security status of the quantum key cloud platform based on the current and predicted security situation values ​​of the quantum key cloud platform.

[0061] In the specific implementation process, by combining quantum computers, genetic algorithms and simulated annealing algorithms, the security situation of quantum key cloud can be effectively assessed and accurately predicted, which greatly improves the stability of quantum key cloud platform security and provides a strong guarantee for protecting the increasingly large and complex cyberspace.

[0062] Example 2

[0063] A quantum key cloud security situation awareness and prediction system includes a data acquisition layer 1, a security situation assessment layer 2, a security situation prediction layer 3, and a security situation early warning layer 4; wherein,

[0064] The data acquisition layer 1 is used to acquire data from the quantum key cloud platform to obtain a classical dataset;

[0065] More specifically, the data acquisition layer 1 uses probes to acquire data from the quantum key cloud platform.

[0066] More specifically, the probes include traffic probes, log probes, asset probes, and vulnerability probes.

[0067] More specifically, it also includes a data preprocessing layer 5, which is used to normalize the classic dataset input to the security posture assessment layer 2.

[0068] The security situation assessment layer 2 is used to combine quantum computers, genetic algorithms and simulated annealing algorithms to optimize classical datasets, obtain prediction results for classical datasets, and assess the current security situation value of the quantum key cloud platform based on the prediction results of classical datasets.

[0069] More specifically, the security situation assessment layer 2 is preset with a platform security level, and the current security situation value of the quantum key cloud platform is assessed based on the platform security level corresponding to the optimization result.

[0070] The security situation prediction layer 3 is used to combine quantum computers, genetic algorithms and simulated annealing algorithms to optimize the current security situation value and historical security situation value of the quantum key cloud platform, so as to obtain the predicted situation value of the quantum key cloud platform.

[0071] More specifically, the quantum computer includes an input layer, a hidden layer, and an output layer:

[0072] The input layer is used to convert the input data into quantum state information and initialize the parameter weights on the quantum circuit.

[0073] The hidden layer is used to measure quantum state information through variable quantum circuitry to obtain an output vector;

[0074] The output layer is used to map the output vector to obtain the prediction result of the input data.

[0075] More specifically, such as Figure 2 As shown, the specific steps for optimization using a combination of quantum computing, genetic algorithms, and simulated annealing algorithms are as follows:

[0076] A1: Randomly generate M individuals as the initial population, where each individual includes n parameter weights, and each parameter weight corresponds to a different node on the quantum circuit of the quantum computer input layer;

[0077] In practice, the number of individuals in the population can be set according to the actual situation; the number of nodes in the quantum circuit of the quantum computer input layer is determined based on the input data, and each node corresponds to a parameter weight, thereby determining the number of parameter weights n;

[0078] A2: Encode the position of the parameter weights in each individual;

[0079] More specifically, in step A2, the positions of the parameter weights are binary encoded, with the positions of used parameter weights encoded as 1 and the positions of unused parameter weights encoded as 0.

[0080] A3: Calculate the fitness value of each individual based on the input and output data of the quantum computer;

[0081] More specifically, in step A3, the fitness value is calculated using the following fitness function:

[0082]

[0083] Where X is the data input to the quantum computer, and Y is the prediction result of X.

[0084] In the specific implementation process, As a fitness function, the selected n parameter weights are used as constraints to ensure that the fitness value f reaches a preset minimum threshold.

[0085] A4: Determine whether the minimum fitness value is not greater than the preset minimum threshold;

[0086] If so, proceed to step A10;

[0087] If not, proceed to step A5;

[0088] A5: Determine if the current iteration count has reached the preset maximum iteration count;

[0089] If so, proceed to step A10;

[0090] If not, proceed to step A6;

[0091] A6: Sort each individual's fitness value from smallest to largest, retain the top-ranked individuals according to a preset ratio, and discard the bottom-ranked individuals;

[0092] A7: Interleave the retained individuals to create new combinations of individuals;

[0093] A8: Randomly mutate the codes of some individuals in the new combination of individuals, for example, change 0 to 1 or 1 to 0;

[0094] A9: The individuals obtained from the mutation are used to form an intermediate population. After simulated annealing of the intermediate population, a new generation of population is obtained. Return to step A3 for the next round of iteration.

[0095] A10: Select the individual with the smallest fitness value as the optimal individual, and the optimization ends.

[0096] The security situation warning layer 4 is used to provide users with real-time warnings about the current security status of the quantum key cloud platform based on the current and predicted security situation values ​​of the quantum key cloud platform.

[0097] Example 3

[0098] like Figure 3 As shown, a quantum key cloud security situation awareness and prediction method, implemented based on the aforementioned quantum key cloud security situation awareness and prediction system, includes the following steps:

[0099] S1: Collect data from the quantum key cloud platform to obtain a classical dataset;

[0100] S2: Optimize the classic dataset to obtain the prediction results for the classic dataset;

[0101] S3: Evaluate the current security posture of the quantum key cloud platform based on the prediction results of the classical dataset;

[0102] S4: Optimize the current and historical security status values ​​of the quantum key cloud platform to obtain the predicted status value of the quantum key cloud platform;

[0103] S5: Provides real-time alerts to users regarding the current security status of the quantum key cloud platform based on its current and predicted security status values.

[0104] Based on the disclosure and teachings of the foregoing specification, those skilled in the art can make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the invention should also fall within the protection scope of the claims of the present invention. Furthermore, although some specific terms are used in this specification, these terms are only for convenience of explanation and do not constitute any limitation on the present invention.

Claims

1. A quantum key cloud security situation awareness prediction system, characterized in that, The security situation evaluation layer, the security situation prediction layer and the security situation early warning layer are combined with the quantum computer, the genetic algorithm and the simulated annealing algorithm. The data acquisition layer is configured to acquire data of the quantum key cloud platform to obtain a classical data set. The security situation evaluation layer is configured to combine the quantum computer, the genetic algorithm and the simulated annealing algorithm to optimize the classical data set to obtain a prediction result of the classical data set, and evaluate a current security situation value of the quantum key cloud platform according to the prediction result of the classical data set. The security situation prediction layer is configured to combine the quantum computer, the genetic algorithm and the simulated annealing algorithm to optimize a current security situation value and a historical security situation value of the quantum key cloud platform to obtain a predicted situation value of the quantum key cloud platform. The quantum computer comprises an input layer, a hidden layer and an output layer. The input layer is configured to convert input data into quantum state information and initialize parameter weights on a quantum circuit. The hidden layer is configured to measure the quantum state information through a variational quantum circuit to obtain an output vector. The output layer is configured to map the output vector to obtain a prediction result of the input data. The specific steps of the optimization processing combined with the quantum computer, the genetic algorithm and the simulated annealing algorithm are as follows: A1: randomly generate M individuals as an initial population, wherein each individual includes n parameter weights, and each parameter weight corresponds to a different node on the quantum circuit of the input layer of the quantum computer; A2: encode the positions of the parameter weights in each individual; A3: calculate the fitness value of each individual according to the input data and the output data of the quantum computer; A4: determine whether the minimum fitness value is not greater than a preset minimum threshold value; if yes, execute step A10; if no, execute step A5; A5: determine whether the current iteration number reaches a preset maximum iteration number; if yes, execute step A10; if no, execute step A6; A6: sort the fitness values of each individual from small to large, retain the individuals with high ranks according to a preset proportion, and discard the individuals with low ranks; A7: cross the retained individuals with each other to generate new individual combinations; A8: randomly mutate the codes of part of the individuals in the new individual combinations; A9: form an intermediate population from the individuals obtained by the mutation, obtain a new generation of population after the simulated annealing of the intermediate population, and return to step A3 for the next iteration; A10: take the individual with the minimum fitness value as an optimal individual, and the optimization ends; The security situation early warning layer is configured to real-time early warn the user about the current security situation of the quantum key cloud platform according to the current security situation value and the predicted situation value of the quantum key cloud platform.

2. The quantum key cloud security situation awareness prediction system of claim 1, wherein, The data acquisition layer acquires data of the quantum key cloud platform by using a probe.

3. The quantum key cloud security situation awareness prediction system of claim 2, wherein, The probe comprises a flow probe, a log probe, an asset probe and a vulnerability probe.

4. The quantum key cloud security situation awareness prediction system of claim 1, wherein, In step A2, the positions of the parameter weights are binary coded, and the positions of the parameter weights used are coded as 1, and the positions of the parameter weights not used are coded as 0.

5. The quantum key cloud security situation awareness prediction system of claim 1, wherein, In step A3, the fitness value is calculated by the following fitness function: wherein X is the data input into the quantum computer, and Y is the prediction result of X.

6. The quantum key cloud security situation awareness prediction system of claim 1, wherein, The data preprocessing layer is further included for data normalization processing on the classical data set input into the security posture assessment layer.

7. The quantum key cloud security situation awareness prediction system of claim 1, wherein, The security posture assessment layer is preset with a platform security level, and the current security posture value of the quantum key cloud platform is evaluated according to the platform security level corresponding to the optimization result. 8.A quantum key cloud security situation awareness prediction method of any one of claims 1-7, wherein, The method comprises the following steps: S1: data acquisition is performed on the quantum key cloud platform to obtain a classical data set; S2: the classical data set is optimized to obtain a prediction result of the classical data set; S3: the current security posture value of the quantum key cloud platform is evaluated according to the prediction result of the classical data set; S4: the current security posture value and the historical security posture value of the quantum key cloud platform are optimized to obtain a predicted posture value of the quantum key cloud platform; S5: the user is real-time warned of the current security situation of the quantum key cloud platform according to the current security posture value and the predicted posture value of the quantum key cloud platform.

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