Network security optimization method based on kink theory

Abstract

The invention discloses a network security optimization method based on a kink theory, which relates to the technical field of network security, and comprises the following steps: quantization mapping of a network structure: regarding each node in a network as a quantum bit, and regarding network connection as a quantum entanglement state; constructing a quantum knot, and creating the quantum knot representing the network state by using the characteristics of quantum calculation; according to the method, more comprehensive security evaluation is provided by quantizing network nodes and connections, performing security analysis by using quantum computing characteristics, creating quantum knots of network states by using quantum computing, and describing security characteristics of the network by developing new quantum invariants. An attacker is simulated to attack a network by using quantum computing, potential vulnerabilities are detected, a quantum optimization algorithm is designed, an optimal security policy is searched, and more efficient security measures are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of network security, and in particular to a network security optimization method based on knot theory. Background Art

[0002] Network security optimization refers to a series of measures and methods to improve the security, reliability and ability of network systems to resist various attacks. It includes improving network infrastructure, applications, data and user behavior to ensure that the network can maintain stable and secure operation when facing potential threats.

[0003] Network security optimization methods need to be customized according to the specific needs and environment of the organization, and need to be constantly updated and adjusted to cope with new threats and challenges. By implementing effective network security optimization methods, organizations can improve the security of their network systems and reduce losses caused by cyber attacks.

[0004] The optimization methods in the existing technology often rely on static network topology and traffic patterns, but ignore the dynamic changes of network status. In security analysis, they often only focus on specific security indicators, such as encryption strength, firewall rules, etc., but ignore the impact of network structure on security.

[0005] Therefore, we propose a network security optimization method based on knot theory to solve the above problems. Summary of the invention

[0006] The purpose of the present invention is to provide a network security optimization method based on knot theory to solve the current market problems raised by the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A network security optimization method based on knot theory, comprising:

[0009] The quantization mapping of the network structure considers each node in the network as a quantum bit and the network connection as a quantum entangled state;

[0010] Quantum knot construction, using the characteristics of quantum computing to create quantum knots that represent the state of the network;

[0011] Quantum invariant computing: developing new quantum invariants to describe the security characteristics of quantum knots and calculating quantum invariants of quantum knots to evaluate the security level of the network;

[0012] Quantum simulation of security vulnerabilities, building quantum attack models to simulate how attackers use quantum computing to attack networks, and using quantum algorithms to detect potential vulnerabilities in quantum knots;

[0013] Quantum optimization of security strategies, using quantum optimization algorithms to find the best strategy to strengthen network security, and converting the output of quantum optimization algorithms into actual security strategies, such as quantum key distribution and quantum encryption;

[0014] Quantum state reconstruction and verification: reconstruct the quantum state of the network according to the security strategy to enhance its security, and use quantum verification technology to confirm the stability and security of the network quantum state;

[0015] Continuous quantum monitoring and adaptive adjustment: Implement continuous quantum monitoring, track changes in network status in real time, and use quantum algorithms to adaptively adjust the network structure based on monitoring data;

[0016] Quantum feedback and learning loop, collects quantum feedback on network performance and security events, and uses quantum machine learning technology to learn from the feedback and optimize quantum network security analysis methods.

[0017] As a further optimization solution of the present invention, the quantization mapping of the network structure includes:

[0018] Treat each node in the network as a quantum bit, capable of existing in multiple states at the same time, namely "0" and "1";

[0019] View network connections as quantum entangled states, representing the complex interactions between nodes.

[0020] As a further optimization solution of the present invention, the quantum tether structure includes:

[0021] Using the properties of quantum computing, quantum knots are created that represent the state of the network.

[0022] As a further optimization solution of the present invention, the quantum invariant calculation includes:

[0023] Develop new quantum invariants to describe the security properties of quantum knots;

[0024] Calculate the quantum invariants of quantum knots and evaluate the security level of the network.

[0025] As a further optimization solution of the present invention, the quantum simulation of the security vulnerability includes:

[0026] Build a quantum attack model to simulate how attackers use quantum computing to attack the network;

[0027] Using quantum algorithms to probe potential holes in quantum knots.

[0028] As a further optimization scheme of the present invention, the quantum optimization of the security strategy includes:

[0029] Design quantum optimization algorithms to find the best strategies to strengthen network security;

[0030] Convert the output of quantum optimization algorithms into practical security strategies, namely into quantum key distribution and quantum encryption.

[0031] As a further optimization scheme of the present invention, the quantum state reconstruction and verification includes:

[0032] Reconstruct the quantum state of the network according to the security strategy to enhance its security;

[0033] Use quantum verification technology to confirm the stability and security of the network's quantum state.

[0034] As a further optimization scheme of the present invention, the continuous quantum monitoring and adaptive adjustment includes:

[0035] Implement continuous quantum monitoring to track changes in network status in real time;

[0036] Based on the monitoring data, quantum algorithms are used to adaptively adjust the network structure.

[0037] As a further optimization scheme of the present invention, the quantum feedback and learning cycle includes:

[0038] Collect quantum feedback on network performance and security events;

[0039] Leverage quantum machine learning techniques to learn from feedback and optimize quantum cybersecurity analysis methods.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] In this invention, by quantizing network nodes and connections, using quantum computing characteristics for security analysis, and using quantum computing to create quantum knots of network states, a more comprehensive security assessment is provided by developing new quantum invariants to describe the security characteristics of the network. Attackers are simulated to use quantum computing to attack the network, potential vulnerabilities are detected, quantum optimization algorithms are designed, the best security strategy is found, and more efficient security measures are implemented.

[0042] The present invention reconstructs the network quantum state according to the security strategy and uses quantum verification technology to ensure the stability and security of the network quantum state. It implements continuous quantum monitoring and adaptively adjusts the network structure based on the monitoring data to deal with security threats. Finally, it collects quantum feedback on network performance and security events and uses quantum machine learning technology to continuously optimize network security analysis methods.

[0043] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Flowchart for constructing quantum entanglement network for QFT and quantum logic gates in the present invention;

[0045] Figure 2 A flowchart of quantum simulation of security vulnerabilities in the present invention;

[0046] Figure 3 A flow chart of using quantum key distribution and quantum encryption technology to protect network data in the present invention;

[0047] Figure 4 This is a flow chart of quantum state reconstruction and verification in the present invention;

[0048] Figure 5 This is a flow chart of quantum monitoring and adaptive adjustment in the present invention;

[0049] Figure 6 The quantum feedback and learning cycle in the present invention is a flowchart for quantum network security analysis. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Embodiment 1

[0052] See also Figure 1-Figure 6 , a network security optimization method based on knot theory, comprising:

[0053] The quantization mapping of the network structure considers each node in the network as a quantum bit and the network connection as a quantum entangled state;

[0054] The quantization mapping of the network structure includes:

[0055] Treat each node in the network as a quantum bit, capable of existing in multiple states at the same time, namely "0" and "1";

[0056] View network connections as quantum entangled states, representing the complex interactions between nodes.

[0057] Quantum knot construction, using the characteristics of quantum computing to create quantum knots that represent the state of the network;

[0058] The quantum nexus structure comprises:

[0059] Using the properties of quantum computing, quantum knots are created that represent the state of the network.

[0060] Quantum invariant computing: developing new quantum invariants to describe the security characteristics of quantum knots and calculating quantum invariants of quantum knots to evaluate the security level of the network;

[0061] The quantum invariant calculation includes:

[0062] Develop new quantum invariants to describe the security properties of quantum knots;

[0063] Calculate the quantum invariants of quantum knots and evaluate the security level of the network.

[0064] Quantum simulation of security vulnerabilities, building quantum attack models to simulate how attackers use quantum computing to attack networks, and using quantum algorithms to detect potential vulnerabilities in quantum knots;

[0065] Quantum simulations of the security vulnerabilities include:

[0066] Build a quantum attack model to simulate how attackers use quantum computing to attack the network;

[0067] Using quantum algorithms to probe potential holes in quantum knots.

[0068] Quantum optimization of security strategies, using quantum optimization algorithms to find the best strategy to strengthen network security, and converting the output of quantum optimization algorithms into quantum key distribution and quantum encryption;

[0069] The quantum optimization of the security strategy includes:

[0070] Design quantum optimization algorithms to find the best strategies to strengthen network security;

[0071] Convert the output of quantum optimization algorithms into practical security strategies, namely into quantum key distribution and quantum encryption.

[0072] Quantum state reconstruction and verification: reconstruct the quantum state of the network according to the security strategy to enhance its security, and use quantum verification technology to confirm the stability and security of the network quantum state;

[0073] The quantum state reconstruction and verification include:

[0074] Reconstruct the quantum state of the network according to the security strategy to enhance its security;

[0075] Use quantum verification technology to confirm the stability and security of the network's quantum state.

[0076] Continuous quantum monitoring and adaptive adjustment: Implement continuous quantum monitoring, track changes in network status in real time, and use quantum algorithms to adaptively adjust the network structure based on monitoring data;

[0077] The continuous quantum monitoring and adaptive adjustment include:

[0078] Implement continuous quantum monitoring to track changes in network status in real time;

[0079] Based on the monitoring data, quantum algorithms are used to adaptively adjust the network structure.

[0080] Quantum feedback and learning loop, collects quantum feedback on network performance and security events, and uses quantum machine learning technology to learn from the feedback and optimize quantum network security analysis methods.

[0081] The quantum feedback and learning cycle includes:

[0082] Collect quantum feedback on network performance and security events;

[0083] Leverage quantum machine learning techniques to learn from feedback and optimize quantum cybersecurity analysis methods.

[0084] Specifically, for the quantization mapping of the network structure, each node in the network is regarded as a quantum bit, and the two states are represented by "|0>" and "|1>". The Bell state "|Φ+>=(|00>+|11>) / √2" is used to represent the entanglement between node A and node B.

[0085] For quantum tether construction, quantum Fourier transform and quantum logic gates are used to construct quantum entanglement networks. QFT is used to convert node quantum states into entangled states, and then different nodes are connected through quantum logic gates to form a quantum entanglement network.

[0086] More specifically, QFT transforms the state of n qubits from the standard ground state {|0>,|1>,…,|2^n-1>} to the frequency ground state {|0>,|1>,…,|2^n-1>}, where each state of the frequency ground state |k> can be expressed as:

[0087] |k>=1 / √2^n*Σ_(j=0)^(2^n-1)e^(2πijk / 2^n)|j>

[0088] The specific steps to construct a quantum entanglement network using QFT and quantum logic gates are:

[0089] Step 1: Apply QFT to each node, that is, convert each node quantum state into a frequency ground state to prepare for the subsequent entangled state generation;

[0090] Step 2: Use quantum logic gates to connect nodes. Use CNOT gates to connect node A and node B to form an entangled state “|00>” or “|11>”.

[0091] Step 3: Adjust the entangled state. Use a T gate or other phase gate to adjust the entangled state to meet the specific requirements of the network structure.

[0092] For quantum invariant calculations, quantum entanglement entropy and quantum entanglement generation rate indicators are used to describe the degree of entanglement and security characteristics of quantum knots, and quantum algorithms are used to calculate quantum entanglement entropy or quantum entanglement generation rate.

[0093] Specifically, the formula used for the quantum entanglement entropy is: S(ρ)=Tr(ρlog2ρ)

[0094] where ρ is the density matrix of the system.

[0095] Mutual information measures the degree of entanglement between two systems and is defined as the difference in the entropy of the two systems:

[0096] I(A:B)=S(ρ_AB)-S(ρ_A)-S(ρ_B)

[0097] Among them, ρ_AB is the joint density matrix of the two systems, and ρ_A and ρ_B are the density matrices of the two systems respectively.

[0098] The linear entropy generation rate measures the rate of change of the linear entropy of a system over time:

[0099] dS(t) / dt=Tr(ρ(t)H(t))

[0100] Where ρ(t) is the density matrix of the system at time t, and H(t) is the Hamiltonian of the system.

[0101] The local entanglement generation rate is used to measure the rate of change of the system's local entanglement over time, and to more accurately describe the system's entanglement generation process. Destroy the network structure. Use quantum algorithms to analyze quantum entangled states and identify vulnerabilities that attackers may exploit.

[0102] Specifically, first, we define quantum entanglement entropy and quantum entanglement generation rate. Quantum entanglement entropy is an indicator that describes the degree of quantum entanglement, measuring the degree of entanglement between different parts of a quantum system. The larger the entanglement entropy, the higher the degree of entanglement and the higher the security of the system. The quantum entanglement generation rate measures the speed at which the system generates entanglement, indicating the system's ability to transform from a non-entangled state to an entangled state.

[0103] Collect network topology data, including the relationship between nodes and edges. Collect network traffic data, including the size, source and destination addresses of data packets. Collect network attack data, including attack type, attack source and attack target.

[0104] Then, a quantum knot model was constructed, where each node in the network was regarded as a quantum bit, and the two states were represented by “|0>” and “|1>” respectively. The connection between the nodes was regarded as a quantum entangled state, and the Bell state “|Φ+>=(|00>+|11>) / √2” was used to represent the entanglement between node A and node B.

[0105] Calculate quantum entanglement entropy and quantum entanglement generation rate, use quantum state tomography technology to measure the quantum entanglement entropy between nodes in the network, use quantum algorithm to calculate the quantum entanglement generation rate between nodes in the network, and use linear entropy generation rate or local entanglement generation rate.

[0106] Analyze the results of quantum entanglement entropy and quantum entanglement generation rate, evaluate the degree of entanglement between nodes in the network, evaluate the security of the network based on the degree of entanglement and security characteristics, and identify potential security risks.

[0107] Based on the analysis results, a strategy for optimizing the network configuration is formulated, and the adjustment strategy is executed on the quantum computer to change the entanglement state between nodes through quantum logic gates.

[0108] Use quantum state tomography technology to measure quantum entanglement entropy and quantum entanglement generation rate again to evaluate the effect of the adjustment. Continue to monitor the network status to ensure that the adjusted state is stable, and prepare to respond to new security threats.

[0109] For quantum simulation of security vulnerabilities, it simulates attackers using quantum entanglement to steal network information or destroy network structures. Quantum algorithms are used to analyze quantum entanglement to identify vulnerabilities that attackers may exploit.

[0110] Specifically, to build a quantum attack model, first, according to the type and structure of the network, select quantum teleportation attack or quantum key distribution attack, and determine the information the attacker wants to steal or the network structure to destroy.

[0111] In the simulated attack process, the attacker establishes an entangled state with a node in the network. The attacker measures its entangled state and sends the measurement results to the network node. The network node recovers the information stolen by the attacker based on the measurement results.

[0112] Use quantum algorithms to analyze quantum entangled states and identify vulnerabilities that attackers may exploit. By analyzing the degree of entanglement and entanglement pattern of the entangled state, we can identify vulnerabilities that attackers may exploit.

[0113] Evaluate the success rate of attackers in stealing information or destroying network structures. Based on the attack effect, evaluate the impact of the attack on network security. Based on the analysis results, add redundant paths or isolate the attack area.

[0114] Execute the adjustment strategy on the quantum computer and change the network structure through quantum logic gates. Use quantum algorithms to analyze the quantum entangled state again and evaluate the effect of the adjustment. Continue to monitor the network status to ensure that the adjusted state is stable, while preparing to respond to new security threats. Record the monitoring data and adjustment process for future analysis and optimization. Based on the feedback data, continuously optimize the adjustment strategy to improve the network's adaptability and security.

[0115] For quantum optimization of security strategies, we use quantum approximate optimization algorithms to find the optimal quantum entangled state configuration to enhance network security. We use quantum key distribution to establish secure communication channels, or use quantum encryption technology to protect network data.

[0116] Specifically, the quantum approximate optimization algorithm is used to find the optimal quantum entangled state configuration. The quantum approximate optimization algorithm is an optimization algorithm based on the principle of quantum annealing, which is used to find the optimal solution to complex problems. In quantum network security analysis, QAOA is used to find the optimal quantum entangled state configuration and enhance the security of the network.

[0117] QAOA simulates the annealing process on a quantum computer to find the optimal solution to the problem. During the annealing process, the system starts from a high temperature state, gradually lowers the temperature, and finally reaches a low temperature state. At this time, the system is in the ground state, which corresponds to the optimal solution to the problem.

[0118] More specifically, we first define the Hamiltonian, define the entanglement entropy and quantum entanglement generation rate indicators as the Hamiltonian, construct the driving Hamiltonian, and drive the system from the ground state to the target state. The QAOA algorithm is executed on a quantum computer to find the optimal quantum entangled state configuration.

[0119] Use quantum key distribution to establish a secure communication channel. Quantum key distribution is a key distribution technology based on quantum entanglement. It can establish a secure communication channel to prevent attackers from eavesdropping and tampering with information.

[0120] QKD uses the characteristics of the quantum no-cloning theorem and the quantum uncertainty principle to ensure the security of the key.

[0121] More specifically, a quantum entanglement source is used to generate an entangled state, which is distributed to both communicating parties. Both communicating parties measure the entangled state and extract the key information. Quantum encryption technology is used to protect network data. Quantum encryption technology is a technology that uses the characteristics of quantum states to encrypt data, protect the security of network data, and prevent attackers from stealing and tampering with data.

[0122] For quantum state reconstruction and verification, the entanglement state between nodes is adjusted according to the value of quantum entanglement entropy to enhance the security of the network. Quantum state tomography technology is used to measure the quantum state of the network, and quantum error correction technology is used to correct the error of the quantum state.

[0123] Specifically, the steps of adjusting the entangled state between nodes according to the value of quantum entanglement entropy are:

[0124] Step 1: Use quantum state tomography technology to measure the entangled state between nodes in the network and calculate the quantum entanglement entropy of the entangled state;

[0125] Step 2: Analyze the value of entanglement entropy and evaluate the degree of entanglement between nodes. When the entanglement entropy value is lower than the threshold, it proves that the degree of entanglement between nodes is insufficient and the security of the network may be affected.

[0126] Step 3: According to the analysis results of entanglement entropy, adjust the entanglement state between nodes to enhance the security of the network. When using the CNOT gate or T gate in the quantum logic gate, the entanglement state between nodes is converted to an entanglement state with a higher degree of entanglement.

[0127] The steps to measure the quantum state of a network using quantum state tomography are:

[0128] Step 1: Select a suitable measurement basis according to the network structure, choose Z basis or X basis;

[0129] Step 2: Use quantum state tomography technology to measure the quantum state of nodes in the network and record the measurement results;

[0130] Step 3: Based on the measurement results, use the quantum state tomography algorithm to reconstruct the quantum states of the nodes in the network.

[0131] The steps to correct quantum state errors using quantum error correction technology are:

[0132] Step 1: Use quantum error correction codes to identify errors in the quantum states of nodes in the network, that is, quantum bit flip codes or quantum phase flip codes can be used to identify errors;

[0133] Step 2: Use quantum logic gates to correct errors in the quantum states of nodes in the network, that is, CNOT gates or T gates can be used to correct errors;

[0134] Step 3: Use quantum state tomography technology to measure the quantum state of the nodes in the network again, verify the error correction effect, and ensure that the error in the quantum state is successfully corrected.

[0135] For continuous quantum monitoring and adaptive adjustment, the entanglement degree of quantum entangled states and the stability of quantum states are monitored. The entangled state configuration between nodes is dynamically adjusted according to the value of quantum entanglement entropy.

[0136] Specifically, quantum monitoring equipment is deployed at key nodes of the network to measure and analyze quantum entangled states and define quantum entanglement entropy and the stability of quantum states.

[0137] The quantum state tomography technology is used to measure the quantum entanglement entropy between nodes in the network in real time, and the stability of the quantum state is analyzed by quantum state tomography technology, including the decoherence time of the quantum state and the evolution speed of the quantum state. The collected monitoring data is analyzed to analyze the abnormal increase of entanglement entropy and the rapid decoherence of the quantum state.

[0138] According to the analysis results, an adaptive adjustment strategy is formulated to adjust the entanglement strength between nodes and change the entanglement mode. The adjustment strategy is executed on the quantum computer to change the entanglement state between nodes through quantum logic gates.

[0139] Use quantum state tomography to measure the quantum entangled state again and evaluate the effect of the adjustment. Continue to monitor the quantum entangled state to ensure that the adjusted state is stable, and be prepared to respond to new security threats. Record the monitoring data and adjustment process for future analysis and optimization. Based on the feedback data, continuously optimize the adjustment strategy to improve the network's adaptability and security. During the monitoring process, when an emergency is found, such as rapid decoherence of the quantum state or abnormal changes in entanglement entropy, take emergency response measures immediately. In an emergency, a more radical adjustment strategy can be adopted to quickly restore the network's security status.

[0140] For quantum feedback and learning loops, network attack events and quantum state error information are recorded. The optimal network security strategy is learned using quantum reinforcement learning algorithms.

[0141] Specifically, it monitors network activities in real time, identifies and records any network attack events, such as data leakage and abnormal traffic. It uses quantum state tomography technology to detect quantum state errors, such as quantum state decoherence and quantum bit flipping. It collects information on attack events and quantum state errors, including the time, location, attack type, and error size of the event. It cleans and preprocesses the collected data to remove noise and redundant information.

[0142] To learn the optimal network security strategy, we first select a quantum reinforcement learning algorithm and build a simulated network environment that contains the behaviors of attackers and defenders. We initialize a strategy for the defender, which defines the actions the defender should take in various states. In the simulated environment, the defender learns the strategy based on the attacker's behavior and reward signals. We evaluate the performance of the learned strategy in the real network environment and adjust the strategy based on the results.

[0143] Adjust and optimize the learned network security strategy based on attack events and quantum state error information. Repeat the learning process until the optimal network security strategy is found. Apply the learned optimal network security strategy to the actual network environment. Verify the effectiveness of the strategy to ensure that the network can resist attacks and remain secure in actual operation. Collect feedback information from network operation, including attack events and quantum state error information. Adjust and optimize the learned network security strategy based on the feedback information. Continuously adjust and optimize the learned network security strategy as the network environment changes. Network defenders need to be able to adapt to new attack methods and environmental changes.

[0144] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0145] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0146] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0147] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0148] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0149] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A network security optimization method based on knot theory, characterized in that: include: The quantization mapping of the network structure considers each node in the network as a quantum bit and the network connection as a quantum entangled state; Quantum knot construction, using the characteristics of quantum computing to create quantum knots that represent the state of the network; Quantum invariant computing: developing new quantum invariants to describe the security characteristics of quantum knots, and calculating the quantum invariants of quantum knots to evaluate the security level of the network; Quantum simulation of security vulnerabilities, building quantum attack models to simulate how attackers use quantum computing to attack networks, and using quantum algorithms to detect potential vulnerabilities in quantum knots; Quantum optimization of security strategies, using quantum optimization algorithms to find the best strategy to strengthen network security and convert the output of quantum optimization algorithms into actual security strategies; Quantum state reconstruction and verification: reconstruct the quantum state of the network according to the security strategy to enhance its security, and use quantum verification technology to confirm the stability and security of the network quantum state; Continuous quantum monitoring and adaptive adjustment: Implement continuous quantum monitoring, track changes in network status in real time, and use quantum algorithms to adaptively adjust the network structure based on monitoring data; Quantum feedback and learning loop, collects quantum feedback on network performance and security events, and uses quantum machine learning technology to learn from the feedback and optimize quantum network security analysis methods.

2. A network security optimization method based on knot theory according to claim 1, characterized in that: The quantization mapping of the network structure includes: Treat each node in the network as a quantum bit, which can exist in multiple states at the same time, namely "0" and "1"; View network connections as quantum entangled states, representing the complex interactions between nodes.

3. The network security optimization method based on knot theory according to claim 1 is characterized in that: The quantum nexus structure comprises: Using the properties of quantum computing, quantum knots are created that represent the state of the network.

4. The network security optimization method based on knot theory according to claim 1 is characterized in that: The quantum invariant calculation includes: Develop new quantum invariants to describe the security properties of quantum knots; Calculate the quantum invariants of quantum knots and evaluate the security level of the network.

5. The network security optimization method based on knot theory according to claim 1 is characterized in that: Quantum simulations of the security vulnerabilities include: Build a quantum attack model to simulate how attackers use quantum computing to attack the network; Using quantum algorithms to probe potential holes in quantum knots.

6. The network security optimization method based on knot theory according to claim 1 is characterized in that: The quantum optimization of the security strategy includes: Design quantum optimization algorithms to find the best strategies to strengthen network security; Convert the output of quantum optimization algorithms into practical security strategies, namely into quantum key distribution and quantum encryption.

7. The network security optimization method based on knot theory according to claim 1 is characterized by: The quantum state reconstruction and verification include: Reconstruct the quantum state of the network according to the security strategy to enhance its security; Use quantum verification technology to confirm the stability and security of the network's quantum state.

8. The network security optimization method based on knot theory according to claim 1 is characterized by: The continuous quantum monitoring and adaptive adjustment include: Implement continuous quantum monitoring to track changes in network status in real time; Based on the monitoring data, quantum algorithms are used to adaptively adjust the network structure.

9. The network security optimization method based on knot theory according to claim 1 is characterized in that: The quantum feedback and learning cycle includes: Collect quantum feedback on network performance and security events; Leverage quantum machine learning techniques to learn from feedback and optimize quantum cybersecurity analysis methods.

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