A network security optimization method based on knot theory

Abstract

This invention discloses a network security optimization method based on knot theory, relating to the field of network security technology. It includes: a quantized mapping of network structure, treating each node in the network as a qubit and network connections as quantum entangled states; and quantum knot construction, utilizing the characteristics of quantum computing to create quantum knots representing network states. In this invention, by quantizing network nodes and connections, security analysis is performed using quantum computing characteristics, and quantum knots representing network states are created using quantum computing. By developing new quantum invariants, the security characteristics of the network are described, providing a more comprehensive security assessment. Simulations of attackers using quantum computing to attack networks are conducted to detect potential vulnerabilities, design quantum optimization algorithms, find optimal security strategies, and implement more efficient security measures.

Description

Technical Field

[0001] This invention relates to the field of network security technology, specifically a network security optimization method based on knot theory. Background Technology

[0002] Network security optimization refers to a series of measures and methods to improve the security, reliability, and resilience of network systems against various attacks. This includes improvements to network infrastructure, applications, data, and user behavior to ensure the network operates stably and securely when facing potential threats.

[0003] Cybersecurity optimization methods need to be tailored to an organization's specific needs and environment, and require continuous updates and adjustments to address new threats and challenges. By implementing effective cybersecurity optimization methods, organizations can improve the security of their network systems and reduce losses caused by cyberattacks.

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

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

[0006] The purpose of this invention is to provide a network security optimization method based on knot theory to solve the problems currently existing in the market as mentioned in the background.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

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

[0009] The quantization mapping of network structure treats each node in the network as a qubit and the network connections as quantum entangled states;

[0010] Quantum knot construction utilizes the properties of quantum computing to create quantum knots that represent network states;

[0011] Quantum invariant computation: Develop new quantum invariants to describe the security properties of quantum knots, and compute quantum invariants of quantum knots to assess the security level of networks;

[0012] Quantum simulation of security vulnerabilities: Constructing a quantum attack model to simulate how attackers can use quantum computing to attack networks and using quantum algorithms to detect potential vulnerabilities in quantum knots;

[0013] Quantum optimization of security strategies involves using quantum optimization algorithms to find the optimal strategy for strengthening network security and then transforming the output of the quantum optimization algorithm into practical security strategies, such as quantum key distribution and quantum encryption.

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

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

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

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

[0018] Each node in the network is considered as a qubit, which can exist in multiple states simultaneously, namely "0" and "1".

[0019] Viewing network connections as quantum entangled states represents the complex interactions between nodes.

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

[0021] By leveraging the properties of quantum computing, quantum knots representing network states can be created.

[0022] As a further optimization 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 to assess the security level of a network.

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

[0026] Construct a quantum attack model to simulate how attackers could use quantum computing to attack networks;

[0027] Use quantum algorithms to detect potential vulnerabilities in quantum knots.

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

[0029] Design quantum optimization algorithms to find the best strategy for strengthening network security;

[0030] The output of quantum optimization algorithms is transformed into practical security strategies, namely quantum key distribution and quantum encryption.

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

[0032] Reconstructing the quantum state of the network according to the security policy enhances its security;

[0033] Quantum verification technology was used to confirm the stability and security of the network's quantum states.

[0034] As a further optimization 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 monitoring data, quantum algorithms are used to adaptively adjust the network structure.

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

[0038] Quantum feedback for collecting network performance and security incidents;

[0039] By leveraging quantum machine learning techniques, we can learn from feedback and optimize quantum network security analysis methods.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] This invention quantizes network nodes and connections, leverages the properties of quantum computing for security analysis, and utilizes quantum computing to create quantum knots in the network state. By developing new quantum invariants, it describes the network's security characteristics and provides a more comprehensive security assessment. The invention also simulates attackers using quantum computing to attack the network, detects potential vulnerabilities, designs quantum optimization algorithms, and seeks the optimal security strategy to achieve more efficient security measures.

[0042] This invention reconstructs the network quantum state based on a security strategy and uses quantum verification technology to ensure the stability and security of the network quantum state. Continuous quantum monitoring is implemented, and the network structure is adaptively adjusted based on monitoring data to address security threats. Finally, quantum feedback on network performance and security events is collected, and quantum machine learning techniques are used to continuously optimize the network security analysis method.

[0043] The above overview 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 invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the construction of a quantum entanglement network using QFT and quantum logic gates in this invention.

[0045] Figure 2 This is a flowchart illustrating the quantum simulation of the security vulnerability in this invention;

[0046] Figure 3 This is a flowchart illustrating the use of quantum key distribution and quantum encryption technology to protect network data in this invention;

[0047] Figure 4 This is a flowchart of the quantum state reconstruction and verification process in this invention;

[0048] Figure 5 This is a flowchart of the quantum monitoring and adaptive adjustment process in this invention;

[0049] Figure 6 The quantum feedback and learning loop in this invention is a flowchart for quantum network security analysis. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1

[0052] Please see Figures 1-6 A network security optimization method based on knot theory includes:

[0053] The quantization mapping of network structure treats each node in the network as a qubit and the network connections as quantum entangled states;

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

[0055] Each node in the network is considered as a qubit, which can exist in multiple states simultaneously, namely "0" and "1".

[0056] Viewing network connections as quantum entangled states represents the complex interactions between nodes.

[0057] Quantum knot construction utilizes the properties of quantum computing to create quantum knots that represent network states;

[0058] The quantum link structure includes:

[0059] By leveraging the properties of quantum computing, quantum knots representing network states can be created.

[0060] Quantum invariant computation: Develop new quantum invariants to describe the security properties of quantum knots, and compute quantum invariants of quantum knots to assess the security level of networks;

[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 to assess the security level of a network.

[0064] Quantum simulation of security vulnerabilities: Constructing a quantum attack model to simulate how attackers can use quantum computing to attack networks and using quantum algorithms to detect potential vulnerabilities in quantum knots;

[0065] The quantum simulation of the security vulnerability includes:

[0066] Construct a quantum attack model to simulate how attackers could use quantum computing to attack networks;

[0067] Use quantum algorithms to detect potential vulnerabilities in quantum knots.

[0068] Quantum optimization of security strategies: Through quantum optimization algorithms, the best strategies for strengthening network security are found, and the output of the quantum optimization algorithms is transformed 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 strategy for strengthening network security;

[0071] The output of quantum optimization algorithms is transformed into practical security strategies, namely quantum key distribution and quantum encryption.

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

[0073] The quantum state reconstruction and verification includes:

[0074] Reconstructing the quantum state of the network according to the security policy enhances its security;

[0075] Quantum verification technology was used to confirm the stability and security of the network's quantum states.

[0076] Continuous quantum monitoring and adaptive adjustment: Implement continuous quantum monitoring to track changes in network state 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 includes:

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

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

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

[0081] The quantum feedback and learning loop includes:

[0082] Quantum feedback for collecting network performance and security incidents;

[0083] By leveraging quantum machine learning techniques, we can learn from feedback and optimize quantum network security analysis methods.

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

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

[0086] More specifically, QFT transforms the states 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 |k> of the frequency ground state can be represented as:

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

[0088] The specific steps for constructing a quantum entangled network using QFT and quantum logic gates are as follows:

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

[0090] Step 2: Connect the nodes using quantum logic gates. 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 T-gates or other phase gates to adjust the entangled state to meet the specific requirements of the network structure.

[0092] For quantum invariant computation, quantum entanglement entropy and quantum entanglement generation rate 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 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 entropy between the two systems:

[0096] I(A:B)=S(ρ_AB)-S(ρ_A)-S(ρ_B)

[0097] Where 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] Linear entropy generation rate measures the rate of change of a system's linear entropy 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 measures the rate of change of local entanglement over time, providing a more accurate description of the entanglement generation process. This disrupts network structure. Quantum algorithms are used 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 describing 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. Quantum entanglement generation rate measures the speed at which the system generates entanglement, representing the system's ability to transition from an entangled state to an entangled state.

[0103] Collect network topology data, including the relationships between nodes and edges. Collect network traffic data, including packet size, source, and destination addresses. Collect attack data, including attack types, attack sources, and attack targets.

[0104] Subsequently, a quantum knot model is constructed, treating each node in the network as a qubit, with "|0>" and "|1>" representing the two states respectively. The connections between nodes are considered as quantum entangled states, and the entanglement between node A and node B is represented by the Bell state "|Φ+>=(|00>+|11>) / √2".

[0105] The quantum entanglement entropy and quantum entanglement generation rate are calculated. Quantum state tomography is used to measure the quantum entanglement entropy between nodes in the network, and quantum algorithms are used to calculate the quantum entanglement generation rate between nodes in the network. Linear entropy generation rate or local entanglement generation rate can be used.

[0106] By analyzing the results of quantum entanglement entropy and quantum entanglement generation rate, the degree of entanglement between nodes in the network is assessed. Based on the degree of entanglement and security characteristics, the security of the network is evaluated, and potential security risks are identified.

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

[0108] Quantum entanglement entropy and quantum entanglement generation rate were measured again using quantum state tomography to assess the effect of the adjustment. The network status was continuously monitored to ensure stability after the adjustment, while preparations were made to address new security threats.

[0109] Quantum simulations of security vulnerabilities simulate attackers exploiting quantum entanglement to steal network information or disrupt network structure. Quantum algorithms are used to analyze entangled states and identify vulnerabilities that attackers may exploit.

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

[0111] The simulated attack process involves an attacker establishing an entangled state with a node in the network. The attacker measures the entangled state and sends the measurement result to the network node. The network node then reconstructs the information stolen by the attacker based on the measurement result.

[0112] Quantum algorithms are used to analyze entangled quantum states to identify vulnerabilities that attackers may exploit. By analyzing the degree and mode of entanglement, potential vulnerabilities can be identified.

[0113] Assess the attacker's success rate in stealing information or disrupting network structure. Based on the attack's effectiveness, evaluate its impact on network security. Based on the analysis results, add redundant paths or isolate the attack area.

[0114] The adjustment strategy is executed on a quantum computer, altering the network structure through quantum logic gates. Quantum algorithms are then used to reanalyze the entangled states and evaluate the effects of the adjustment. The network state is continuously monitored to ensure stability after the adjustment, while preparing for potential new security threats. Monitoring data and the adjustment process are recorded for future analysis and optimization. Based on feedback data, the adjustment strategy is continuously optimized to improve the network's adaptability and security.

[0115] For quantum optimization of security strategies, quantum approximation optimization algorithms are used to find the optimal configuration of quantum entangled states, thereby enhancing network security. Quantum key distribution is used to establish secure communication channels, or quantum encryption techniques are employed to protect network data.

[0116] Specifically, quantum approximation optimization algorithms are used to find the optimal configuration of quantum entangled states. These algorithms, based on the principle of quantum annealing, are used to find optimal solutions to complex problems. In quantum network security analysis, QAOA is used to find the optimal configuration of quantum entangled states, thereby enhancing network security.

[0117] QAOA searches for the optimal solution to a problem by simulating the annealing process on a quantum computer. During annealing, the system starts at a high temperature and gradually decreases in temperature until it reaches a low temperature, at which point the system is in its ground state, which corresponds to the optimal solution to the problem.

[0118] More specifically, we first define the Hamiltonian, using entanglement entropy and quantum entanglement generation rate as Hamiltonians, and then construct a driving Hamiltonian to propel the system from the ground state to the target state. Finally, we execute the QAOA algorithm on a quantum computer to find the optimal configuration of quantum entangled states.

[0119] Quantum key distribution (QKD) is a key distribution technology based on quantum entanglement. It can establish secure communication channels and prevent attackers from eavesdropping on and tampering with information.

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

[0121] More specifically, entangled states are generated using a quantum entanglement source, distributed to both communicating parties, who then measure the entanglement and extract the key information. Quantum encryption technology is used to protect network data; it utilizes the properties of quantum states to encrypt data, ensuring its security and preventing attackers from stealing or tampering with it.

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

[0123] Specifically, the steps for adjusting the entangled states between nodes based on the value of quantum entanglement entropy are as follows:

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

[0125] Step 2: Analyze the entanglement entropy value to assess 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: Based on the analysis results of entanglement entropy, adjust the entangled states between nodes to enhance network security. This can be achieved by using CNOT gates or T gates in quantum logic to transform the entangled states between nodes into more highly entangled states.

[0127] The steps for measuring the quantum states of a network using quantum state tomography are as follows:

[0128] Step 1: Select a suitable measurement basis based on the network structure, choosing either the Z basis or the X basis;

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

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

[0131] The steps for correcting errors in quantum states using quantum error correction technology are as follows:

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

[0133] Step 2: Use quantum logic gates to correct errors in the quantum states of nodes in the network, i.e., use CNOT gates or T gates to correct errors;

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

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

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

[0137] Quantum state tomography (QST) is used to measure the quantum entanglement entropy between nodes in a network in real time. QST is then used to analyze the stability of quantum states, including decoherence time and evolution rate. The collected monitoring data is analyzed to identify anomalous increases in entanglement entropy and rapid decoherence of quantum states.

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

[0139] The quantum entangled state is measured again using quantum state tomography to evaluate the effect of the adjustment. The quantum entangled state is continuously monitored to ensure its stability after adjustment, while preparing for new security threats. Monitoring data and the adjustment process are recorded for future analysis and optimization. Based on feedback data, the adjustment strategy is continuously optimized to improve the network's adaptability and security. During monitoring, if an emergency is detected, such as rapid decoherence of the quantum state or abnormal changes in entanglement entropy, immediate emergency response measures are taken. In emergency situations, more aggressive adjustment strategies can be adopted to quickly restore the network to a secure state.

[0140] For the quantum feedback and learning loop, error information of network attack events and quantum states is recorded. The optimal network security strategy is learned using a quantum reinforcement learning algorithm.

[0141] Specifically, it involves real-time monitoring of network activity to identify and record any network attack events, such as data breaches and abnormal traffic. Quantum state tomography is used to detect errors in quantum states, such as decoherence and qubit flipping. Attack events and quantum state error information are collected, including the time, location, attack type, and magnitude of the error. The collected data is then cleaned and preprocessed to remove noise and redundant information.

[0142] To learn optimal network security strategies, the first step is to select a quantum reinforcement learning algorithm and construct a simulated network environment that includes the behaviors of both attackers and defenders. A policy is initialized for the defender, defining the actions the defender should take in various states. In the simulated environment, the defender learns the policy based on the attacker's behavior and reward signals. The performance of the learned policy in a real network environment is evaluated, and the policy is adjusted based on the results.

[0143] Based on attack events and quantum state error information, the learned network security strategy is adjusted and optimized. This learning process is repeated until the optimal network security strategy is found. The optimized strategy is then applied to a real-world network environment. The effectiveness of the strategy is verified to ensure the network can withstand attacks and remain secure in actual operation. Feedback information from network operation, including attack events and quantum state error information, is collected. Based on this feedback, the learned network security strategy is adjusted and optimized. As the network environment changes, the learned network security strategy is continuously adjusted and optimized. Network defenders need to be able to adapt to new attack methods and environmental changes.

[0144] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

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

[0146] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0147] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0148] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0149] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to 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 network structure treats each node in the network as a qubit and the network connections as quantum entangled states; Quantum knot construction utilizes the properties of quantum computing to create quantum knots that represent network states; Quantum invariant computation involves developing new quantum invariants to describe the security properties of quantum knots and calculating the quantum invariants of quantum knots to evaluate the security level of networks. Specifically, for quantum invariant computation, quantum entanglement entropy and quantum entanglement generation rate are used to describe the degree of entanglement and security properties of quantum knots, and quantum algorithms are used to calculate quantum entanglement entropy or quantum entanglement generation rate. Quantum simulation of security vulnerabilities: Constructing a quantum attack model to simulate how attackers can use quantum computing to attack networks and using quantum algorithms to detect potential vulnerabilities in quantum knots; Quantum optimization of security strategies: This involves using quantum optimization algorithms to find the optimal strategy for strengthening network security and then converting the output of the quantum optimization algorithm into a practical security strategy. Quantum state reconstruction and verification: Reconstructing the quantum state of the network according to the security strategy to enhance its security, and using quantum verification technology to confirm the stability and security of the network's quantum state; Continuous quantum monitoring and adaptive adjustment: Implement continuous quantum monitoring to track changes in network state in real time, and use quantum algorithms to adaptively adjust the network structure based on monitoring data; The quantum feedback and learning loop collects quantum feedback on network performance and security events, and utilizes quantum machine learning techniques to learn from the feedback and optimize quantum network security analysis methods.

2. The network security optimization method based on knot theory according to claim 1, characterized in that: The quantum mapping of the network structure includes: Each node in the network is considered as a qubit, which can exist in multiple states simultaneously, namely "0" and "1". Viewing network connections as quantum entangled states represents the complex interactions between nodes.

3. The network security optimization method based on knot theory according to claim 1, characterized in that: The quantum link structure includes: By leveraging the properties of quantum computing, quantum knots representing network states can be created.

4. The network security optimization method based on knot theory according to claim 1, 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 to assess the security level of a network.

5. The network security optimization method based on knot theory according to claim 1, characterized in that: The quantum simulation of the security vulnerability includes: Construct a quantum attack model to simulate how attackers could use quantum computing to attack networks; Use quantum algorithms to detect potential vulnerabilities in quantum knots.

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

7. A network security optimization method based on knot theory according to claim 1, characterized in that: The quantum state reconstruction and verification includes: Reconstructing the quantum state of the network according to the security policy enhances its security; Quantum verification technology was used to confirm the stability and security of the network's quantum states.

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

9. A network security optimization method based on knot theory according to claim 1, characterized in that: The quantum feedback and learning loop includes: Quantum feedback for collecting network performance and security incidents; By leveraging quantum machine learning techniques, we can learn from feedback and optimize quantum network security analysis methods.

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