A method and system for intelligent encrypted communication between wireless routers

Abstract

The present invention proposes a method and system for intelligent encrypted communication between wireless routers, the method comprising: initializing the network environment, starting the wireless router, using advanced network scanning tools to collect network parameters; building a dynamic decision model; generating quantum bits and adjusting the coding, optimizing the wireless quantum channel, and performing error rate testing and privacy amplification on the quantum bits to achieve security reinforcement; real-time monitoring of encrypted communication, calculating security scores, adaptively adjusting corresponding responses, and performing dynamic threshold management; establishing a blockchain network, and using a quantum key distribution system to generate keys; building a performance detection module, collecting key performance indicators in real time to calculate processing performance; implementing regular security audit procedures and configuring system performance monitoring tools, and developing improvement measures and generating audit and performance evaluation reports based on the results of security audits and performance monitoring. The present invention provides a comprehensive, efficient and highly secure solution for encrypted communication between wireless routers.

Description

Technical Field

[0001] The invention belongs to the field of network security, and in particular relates to an intelligent encryption communication method and system for wireless routers. Background Art

[0002] In the current network security field, encrypted communication technology between wireless routers is the key to ensuring data transmission security and preventing unauthorized access. With the rapid development of applications such as the Internet of Things (IoT), remote office, and smart home, the use of wireless networks has become more and more popular, which has also brought unprecedented challenges to network security. Although traditional encryption technologies such as WPA2 and WPA3 provide basic security protection for wireless communications, with the increase in computing power, the vulnerabilities of these encryption technologies have been gradually discovered, and security has been challenged. For example, the WPA2 protocol was found to have a serious KRACK attack vulnerability in 2017, indicating that even the most widely used security protocols cannot completely defend against advanced attack technologies.

[0003] In addition, most existing wireless encryption methods are based on static key distribution and fixed encryption algorithms, which results in insufficient adaptability and flexibility in the face of dynamically changing network environments and diverse threats. For example, when network traffic increases dramatically or encounters a specific type of DDoS attack, these systems do not respond promptly enough to effectively adjust encryption strategies to adapt to changes in network conditions. In addition, the current encryption technology manages keys in a centralized manner, which poses the risk of key leakage and management abuse, which is particularly prominent in a multi-router environment.

[0004] In response to these problems, the existing technology has not yet provided a comprehensive solution that fully utilizes artificial intelligence to optimize encryption strategy selection and key management. Existing solutions often focus on improving security or efficiency in a single aspect, while ignoring the need for systematic security policy adjustment and intelligent response. Therefore, there is an urgent need for a high-security encrypted communication system that can adapt to changes in network conditions in real time, intelligently manage encryption keys, and effectively resist new network attacks. Summary of the invention

[0005] The purpose of the present invention is to propose an intelligent encrypted communication method and system for wireless routers, which provides a comprehensive, efficient and highly secure solution for encrypted communication between wireless routers, can effectively overcome the various shortcomings of the prior art, and especially performs well in a dynamic network environment.

[0006] In order to achieve the above object, a first aspect of the present invention provides an intelligent encryption communication method for wireless routers, which is applied to an intelligent encryption communication system. The method comprises:

[0007] Deploy multi-point bandwidth sensors at key network nodes, initialize the network environment, start the wireless router, use advanced network scanning tools to collect network parameters, analyze the parameters in combination with real-time security monitoring and historical data, and use dynamic window technology to count and evaluate security events;

[0008] A dynamic decision model based on a multi-armed bandit algorithm is constructed according to network parameters. At the same time, the dynamic decision model dynamically selects an encryption algorithm according to changes in the network environment, as shown below:

[0009]

[0010] Where A(t) represents the encryption algorithm selected at time t, C b (a,B(t)),C n (a, N(t)) and C e (a, E(t, T)) represent the cost functions of encryption algorithm a under the current bandwidth, number of device connections, and security event status, respectively. λ1, λ2, and λ3 represent weight factors used to balance different costs.

[0011] According to the encryption algorithm, the wireless router is equipped with a quantum light source and a quantum state detector to generate quantum bits and adjust the code. At the same time, optical and quantum isolation technologies are used to optimize the wireless quantum channel, and error rate tests and privacy amplification of quantum bits are performed to achieve security reinforcement. Among them, the quantum state is defined as:

[0012]

[0013] Among them, |Φ|> represents the quantum state;

[0014] Monitor encrypted communications in real time based on the communication flow after quantum encryption is activated, deploy an anomaly detection model based on deep learning to calculate the security score, and adaptively adjust the corresponding response based on the security score; when the anomaly detection model detects an anomaly, build a deep behavior analysis and anomaly detection model to identify behaviors that deviate from normal behavior patterns, and design an adaptive response mechanism based on the results of the deep behavior analysis and anomaly detection model to perform dynamic threshold management;

[0015] Establish a blockchain network between routers, use the quantum key distribution system to generate keys, and then exchange the keys to the blockchain network through a secure communication channel. Store the generated keys in encrypted form in the blockchain, and update them regularly based on security policies or revoke them when security incidents are detected.

[0016] Build a performance detection module to collect key performance indicators in real time to calculate processing performance, regularly adjust system parameters based on processing performance, and implement a regular update strategy and feedback mechanism to update and optimize the system;

[0017] Implement regular security audit procedures and configure system performance monitoring tools, develop improvement measures and generate audit and performance evaluation reports based on the results of security audits and performance monitoring.

[0018] Preferably, the dynamic window technology is used to count and evaluate security events, which is expressed as follows:

[0019]

[0020] Among them, α(τ, t) represents a time decay function, which is used to weigh the timeliness of events; E(t, T) represents the total number of weighted security events in the past T time from the deadline t; e(τ) represents the number of security events occurring at time τ.

[0021] Preferably, the wireless quantum channel is optimized by using optical and quantum isolation technology, and the transmission efficiency is expressed as:

[0022] η=e -αLγ(E)

[0023] Among them, η represents the quantum transmission efficiency, α represents the environmental attenuation coefficient, L represents the transmission distance, and γ(E) represents the additional loss caused by environmental noise;

[0024] A highly sensitive single-photon detector is used to perform quantum measurement in a wireless communication environment, where the key M i The extraction is represented as:

[0025] M i =|i> <i|,i∈{0,1}

[0026] Here, i represents the calculation basis.

[0027] Preferably, the error rate test and privacy amplification of the quantum bit are performed to achieve security reinforcement, and the error correction is expressed as:

[0028]

[0029] Among them, P crror represents the key error rate, k i and k′ i They represent the key bits obtained by the two routers from quantum measurement, and N represents the total number of bits.

[0030] Preferably, the safety score is expressed as:

[0031] S=f(v)

[0032] Among them, S is the security score of the traffic, f is the function of the deep learning model, and v represents the features extracted from the traffic.

[0033] Preferably, the detection formula of the deep behavior analysis and anomaly detection model is expressed as follows:

[0034]

[0035] Where D(t) represents the anomaly score at time t, h j represents the feature extraction function, v t represents the feature vector at time t, w j represents the weight parameter, θ represents the dynamically adjusted decision threshold, and σ represents the activation function;

[0036] The adaptive response mechanism is expressed as follows:

[0037]

[0038] Among them, δ1 and δ2 represent the response thresholds adjusted according to the network status and security requirements;

[0039] The adjustment of the dynamic threshold management is expressed as follows:

[0040]

[0041] Among them, α represents the learning rate, τ represents the target anomaly detection rate, and T represents the time window considered.

[0042] Preferably, the blockchain network construction is represented as follows:

[0043] B i = hash(B i-1 +T i +S i-1 )

[0044] Among them, B i is the i-th block, B i-1 is the previous block, T i is the key exchange transaction included in the current block, S i-1 is the state of the previous block;

[0045] The key is generated using the quantum key distribution system, and then exchanged to the blockchain network through a secure communication channel, expressed as:

[0046] K AB =QKD(A, B)

[0047] Among them, K AB represents the key generated by routers A and B through quantum key distribution technology;

[0048] The generated key is stored in the blockchain in an encrypted form. When the key is used, the system will verify the key, which is expressed as:

[0049] V(k)=verify(K AB , hash(K AB ))

[0050] Among them, V(k) represents the verification operation, ensuring that the key K stored in the blockchain AB Matches its hash value;

[0051] Based on security policies, keys are updated regularly or revoked when security incidents are detected, expressed as:

[0052] U(k)=updateorrevoke(K AB , newkey)

[0053] Among them, U(h) represents the update or revocation operation, which generates a new key newkey and updates the blockchain record as needed.

[0054] Preferably, in the performance detection module, the formula for calculating the processing performance is as follows:

[0055]

[0056] Where P(t) represents the average processing performance at time t, t i represents the processing time of the ith operation, and n represents the number of operations in the measurement period;

[0057] The system parameters are adjusted regularly according to the performance indicators, which is expressed as:

[0058] Θ new =Θ old +ηΔP

[0059] Among them, Θ new and θ old Represent the new and old parameter values ​​respectively, η represents the adjustment step size (learning rate), and ΔP represents the performance change.

[0060] Preferably, the security audit procedure is expressed as:

[0061]

[0062] Among them, A i represents the total risk score of the ith audit, a ij represents the risk score of a single inspection item j, and m represents the total number of inspection items;

[0063] The calculation of the configuration system performance monitoring tool is expressed as:

[0064]

[0065] Among them, P(t) represents the average performance index at time t, x k (t) represents the observed value of the kth performance indicator at time t, and n represents the number of monitored performance indicators.

[0066] In a second aspect of the present invention, there is provided an intelligent encrypted communication system between wireless routers, the system comprising:

[0067] Initialization module, used to deploy multi-point bandwidth sensors at key network nodes, initialize the network environment, start the wireless router, use advanced network scanning tools to collect network parameters, analyze the parameters in combination with real-time security monitoring and historical data, and use dynamic window technology to count and evaluate security events;

[0068] The model building module is used to build a dynamic decision model based on the multi-armed bandit algorithm according to network parameters. At the same time, the dynamic decision model dynamically selects the encryption algorithm according to the changes in the network environment, which is expressed as follows:

[0069]

[0070] Where A(t) represents the encryption algorithm selected at time t, C b (a,B(t)),C n (a, N(t)) and C e (a, E(t, T)) represent the cost functions of encryption algorithm a under the current bandwidth, number of device connections, and security event status, respectively. λ1, λ2, and λ3 represent weight factors used to balance different costs.

[0071] The encryption module is used to equip the wireless router with a quantum light source and a quantum state detector to generate quantum bits and adjust the code according to the encryption algorithm, and optimize the wireless quantum channel by using optical and quantum isolation technology, and perform error rate testing and privacy amplification on the quantum bits to achieve security reinforcement; wherein the quantum state is defined as:

[0072]

[0073] Among them, |Φ|> represents the quantum state;

[0074] The encrypted communication module is used to monitor the encrypted communication in real time according to the communication flow after the quantum encryption is started, deploy an anomaly detection model based on deep learning to calculate the security score, and adaptively adjust the corresponding response according to the security score; when the anomaly detection model detects an anomaly, a deep behavior analysis and anomaly detection model is constructed to identify behaviors that deviate from the normal behavior pattern, and an adaptive response mechanism is designed according to the results of the deep behavior analysis and anomaly detection model to perform dynamic threshold management;

[0075] Build a blockchain network module to establish a blockchain network between routers, generate keys using a quantum key distribution system, and then exchange the keys to the blockchain network through a secure communication channel. The generated keys are stored in an encrypted form in the blockchain, and the keys are regularly updated based on security policies or revoked when security incidents are detected;

[0076] Performance detection module, which is used to collect key performance indicators in real time to calculate processing performance, regularly adjust system parameters according to processing performance, and implement a regular update strategy and feedback mechanism to update and optimize the system;

[0077] The audit and evaluation module is used to implement regular security audit procedures and configure system performance monitoring tools, develop improvement measures and generate audit and performance evaluation reports based on the results of security audits and performance monitoring.

[0078] The beneficial technical effects of the present invention are at least as follows:

[0079] (1) The present invention integrates a multi-armed tiger algorithm, and the system can dynamically select the most suitable encryption method according to the network status and security requirements. This solves the problem of insufficient adaptability to dynamic network environments in the prior art and improves the response speed and effect of the system when facing sudden changes in network traffic or attacks.

[0080] (2) The present invention uses deep learning models to monitor communication traffic in real time, and promptly identifies and responds to potential security threats, such as DDoS attacks or data leaks. Compared with traditional static security protection methods, this method can more accurately predict and defend against new and complex network attacks.

[0081] (3) The present invention introduces quantum key distribution technology and uses the quantum uncertainty principle to generate a key that is theoretically uncrackable, thereby fundamentally enhancing the security of the key and solving the key cracking risks that traditional encryption technology may face.

[0082] (4) The present invention adopts decentralized blockchain technology to manage encryption keys, which not only improves the transparency and security of key management, but also effectively prevents the key abuse and leakage problems that may be caused by centralized key management. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0084] Figure 1 The present invention is a flow chart of an intelligent encryption communication method between wireless routers. DETAILED DESCRIPTION

[0085] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0086] like Figure 1 As shown, an embodiment of the present invention provides an intelligent encryption communication method for wireless routers, the method comprises the following steps S1-S7:

[0087] S1. Deploy multi-point bandwidth sensors at key network nodes, initialize the network environment, start the wireless router, use advanced network scanning tools to collect network parameters, analyze the parameters in combination with real-time security monitoring and historical data, and use dynamic window technology to count and evaluate security events;

[0088] S2. Construct a dynamic decision model based on the multi-armed bandit algorithm according to network parameters. At the same time, the dynamic decision model dynamically selects an encryption algorithm according to changes in the network environment, as shown below:

[0089]

[0090] Where A(t) represents the encryption algorithm selected at time t, C b (a,B(t)),C n (a, N(t)) and C e (a, E(t, T)) represent the cost functions of encryption algorithm a under the current bandwidth, number of device connections, and security event status, respectively. λ1, λ2, and λ3 represent weight factors used to balance different costs.

[0091] S3. According to the encryption algorithm, the wireless router is equipped with a quantum light source and a quantum state detector to generate quantum bits and adjust the code. At the same time, optical and quantum isolation technologies are used to optimize the wireless quantum channel, and error rate tests and privacy amplification of quantum bits are performed to achieve security reinforcement; wherein the quantum state is defined as:

[0092]

[0093] Among them, |Φ|> represents the quantum state;

[0094] S4. Monitor encrypted communications in real time according to the communication flow after quantum encryption is started, deploy an anomaly detection model based on deep learning to calculate the security score, and adaptively adjust the corresponding response according to the security score; when the anomaly detection model detects an anomaly, build a deep behavior analysis and anomaly detection model to identify behaviors that deviate from normal behavior patterns, and design an adaptive response mechanism based on the results of the deep behavior analysis and anomaly detection model to perform dynamic threshold management;

[0095] S5. Establish a blockchain network between routers, generate keys using a quantum key distribution system, and then exchange the keys to the blockchain network through a secure communication channel. Store the generated keys in an encrypted form in the blockchain, and periodically update the keys based on security policies or revoke the keys when a security incident is detected.

[0096] S6. Build a performance detection module to collect key performance indicators in real time to calculate processing performance, regularly adjust system parameters based on processing performance, and implement a regular update strategy and feedback mechanism to update and optimize the system;

[0097] S7. Implement regular security audit procedures and configure system performance monitoring tools. Based on the results of security audits and performance monitoring, develop improvement measures and generate audit and performance evaluation reports.

[0098] Each step is explained below.

[0099] In step S1, system initialization and environmental perception provide the entire system with necessary basic data and initial state information, which is the premise for subsequent intelligent operations. Through the preliminary collected data, the basic status of the current network can be understood, which provides data support for selecting the most appropriate encryption strategy.

[0100] When the system starts, it first performs a self-detection to collect basic network parameters such as bandwidth, number of devices, security event history, etc.

[0101] In the embodiment of the present invention, multiple bandwidth monitoring sensors are deployed at key network nodes to collect detailed network traffic data. This includes real-time monitoring of inbound and outbound traffic at each major node, using a time-varying function to describe the bandwidth:

[0102]

[0103] Where b(τ) represents the instantaneous bandwidth usage, Δt is the time length of the measurement window, and B(t) is the average bandwidth usage from time t to t+Δt. For stability and to reduce the impact of noise, time integration is used.

[0104] Furthermore, using advanced network scanning tools, we can not only count the total number of connected devices, but also classify and count different types of devices (such as mobile devices, IoT devices, etc.), as shown below:

[0105]

[0106] Among them, n k (t) is the number of connections of the kth type of device at time t, and N(t) is the total number of device connections at monitoring time t.

[0107] At the same time, combined with real-time security monitoring and historical data analysis, dynamic window technology is used to count and evaluate security events, as shown below:

[0108]

[0109] Among them, α(τ, t) is a time decay function used to weigh the timeliness of the event, E(t, T) is the total number of weighted security events in the past T time before the deadline t, and e(τ) is the number of security events that occurred at time τ.

[0110] In the embodiment of the present invention, in step S2, the data from step 1 is collected, including bandwidth utilization B(t), number of device connections N(t), and total weighted security events E(t, T), and then the data is standardized to ensure that the data input into the decision model has a consistent format and proportion.

[0111] Furthermore, a decision model is constructed to evaluate the adaptability of different encryption algorithms to the current network status. The decision variable A(t) is used to represent the encryption algorithm selected at time t. The decision model dynamically selects the encryption algorithm according to the changes in the network environment:

[0112]

[0113] Where A(t): encryption algorithm selected at time t, C b (a,B(t)),C n (a, N(t)), and C e (a, E(t, T)) are the cost functions of encryption algorithm a under the current bandwidth, number of device connections and security event status, respectively. λ1, λ2, λ3 are weight factors used to balance different costs.

[0114] Based on the model output, the encryption algorithm that best suits the current network environment is implemented. The system periodically re-evaluates the selection to adapt to changes in network conditions.

[0115] Through this method, the system can dynamically select the optimal encryption algorithm based on real-time network data and multi-factor cost analysis, thereby ensuring that the communication between wireless routers can maximize efficiency and performance while ensuring security. This solution not only adapts to complex and changing network environments, but also enables fine control and optimization of encryption strategies.

[0116] In step S3, the quantum key distribution module is started to securely generate and distribute keys.

[0117] Equip wireless routers with specialized quantum light sources and quantum state detectors. These devices are specifically designed to generate and detect quantum bits in a wireless communication environment. Quantum state definition:

[0118]

[0119] This entangled state was chosen because of its high fault tolerance and resistance to environmental perturbations in quantum key distribution, which is very suitable for interference that may be encountered in wireless environments.

[0120] Then, advanced optical and quantum isolation technologies are used to optimize the reliability of quantum bit transmission through the air or optical fiber between wireless routers. The transmission efficiency formula is:

[0121] η=e -αLγ(E) (6)

[0122] Where η is the quantum transmission efficiency, α is the environmental attenuation coefficient, L is the transmission distance, and γ(E) represents the additional loss caused by environmental noise (such as electromagnetic interference).

[0123] The measurement strategy uses a single-photon detector with high sensitivity to ensure high-accuracy quantum measurement in a wireless communication environment. Key extraction formula:

[0124] M i =|i> <i|,i∈{0,1} (7)

[0125] Quantum measurements are performed on a computational basis to extract key bits from entangled qubits.

[0126] Under some examples, the quantum bit error rate test and privacy amplification steps are implemented, and the error correction formula is:

[0127]

[0128] Where P crror is the key error rate, k i and k′ i are the key bits obtained by the two routers from quantum measurement, and N is the total number of bits.

[0129] As a preferred embodiment of the present invention, the quantum key distribution scheme provides an infrastructure for intelligent encrypted communication between wireless routers, and provides a quantum-level security solution, especially for physical and electromagnetic interference problems in wireless environments. By utilizing quantum entanglement and efficient quantum state measurement, this scheme can establish a highly secure key distribution system between wireless routers, and maintain the security and privacy of communication even in the face of complex external interference and potential security threats. In addition, each component and step in the scheme has been carefully designed to adapt to the characteristics of wireless communication, including distance changes, signal attenuation and environmental noise, to ensure efficient operation and security protection of the entire system.

[0130] In step S4, based on the communication flow after quantum encryption is initiated, encrypted communication is monitored in real time, and a deep learning model is deployed to identify abnormal patterns in the data flow.

[0131] Specifically, a data capture module is installed on top of each router node to collect encrypted communication data streams in real time. This module has advanced filtering capabilities and only collects data packets related to security and performance evaluation, reducing interference from irrelevant data.

[0132] The collected data is feature extracted and converted into a format suitable for machine learning analysis. Features include statistical information such as packet size, sending frequency, and transmission delay. The feature vector is represented as:

[0133] v=(v1,v2,...,v n ) (9)

[0134] where v i Represents the i-th feature extracted from the traffic.

[0135] Preferably, a deep learning network, such as a convolutional neural network (CNN), is used to analyze the feature vectors to identify possible security threats or performance bottlenecks. The model output is defined as a security score, which indicates the probability of abnormal traffic:

[0136] S=f(v) (10)

[0137] Where S is the security score of the traffic, and f is the function of the deep learning model. The higher the output value, the more likely the traffic contains anomalies.

[0138] In some examples, based on the security score S output by the model, if an anomaly is detected (for example, S exceeds a predetermined threshold), the system automatically triggers security protocols, such as enhancing encryption strength or redirecting meteors. Specific measures are implemented, such as adjusting encryption algorithms or blocking suspicious traffic, to ensure the continued security and stability of the network.

[0139] The solution of the present invention enhances the security of the intelligent encrypted communication system by realizing efficient data flow monitoring and real-time anomaly detection between wireless routers. The use of deep learning models for anomaly detection not only improves the recognition accuracy, but also enables the system to self-learn and adapt to new threat patterns. In addition, the real-time response mechanism ensures that once a potential threat is detected, the system can take prompt measures, thereby effectively preventing the exploitation of security vulnerabilities and the occurrence of network attacks. The entire solution, from data capture to licensed extraction, to deep learning analysis and real-time response, constitutes a closed-loop monitoring system to ensure that wireless encrypted communications are not only safe but also efficient. Especially in the encrypted communication scenario between wireless routers, this solution uses the latest Al technology to solve the problem of data security monitoring, providing an innovative solution to cope with complex and changing network environments and increasing security threats.

[0140] Furthermore, when an abnormal situation is detected, an efficient data collection module is immediately deployed inside the router to continuously monitor all encrypted traffic passing through. The data is analyzed to extract behavioral features, including but not limited to traffic density, access patterns, time series variation, etc. Using deep learning techniques such as long short-term memory networks (LSTM) or variational autoencoders (VAE), models are built to identify behaviors that deviate from normal behavior patterns. The model is regularly fine-tuned with the latest data to adapt to new communication patterns and potential threats. The detection formula is expressed as follows:

[0141]

[0142] Where D(t) represents the anomaly score at time t, h j is the feature extraction function, v t is the eigenvector at time t, w j is the weight parameter, θ is the dynamically adjusted decision threshold, and σ is the activation function.

[0143] In some examples, based on the anomaly detection score D(t), ARS determines the appropriate response measures. The response measures are dynamically selected based on the severity and type of the anomaly, including but not limited to increasing the encryption level, rerouting traffic, or temporarily isolating specific communications for further analysis. The response formula is as follows:

[0144]

[0145] Among them, δ1 and δ2 are response thresholds adjusted according to network status and security requirements.

[0146] Among them, dynamic threshold management is based on historical data and real-time feedback of the current network environment. DTM adjusts the thresholds of detection and response. This ensures the sensitivity and adaptability of the system to network conditions and security threats. The adjustment formula is:

[0147]

[0148] Where α is the learning rate, τ is the target anomaly detection rate, and T is the time window considered.

[0149] In step S5, blockchain technology is used to record and verify the life cycle of all keys.

[0150] Specifically, a dedicated blockchain network is established between all participating routers. This blockchain will be used to manage and verify all exchanged keys to ensure that they are tamper-proof and traceable, as shown below:

[0151] B i = hash(B i-1 +T i +S i-1 ) (14)

[0152] Among them, B i is the i-th block, B i-1 is the previous block, T i is the key exchange transaction included in the current block, S i-1 It is the state of the previous block.

[0153] Furthermore, the quantum key distribution system is used to generate the key, and then the key is exchanged to the blockchain network through a secure communication channel, as shown below:

[0154] K AB =QKD(A, B) (15)

[0155] Among them, K AB It is the key generated by routers A and B through quantum key distribution technology.

[0156] The generated key is stored in the blockchain in an encrypted form. Whenever the key is used, the system verifies the validity and integrity of the key, as shown below:

[0157] V(k)=verify(K AB , hash(K AB )) (16)

[0158] Among them, V(k) is the verification operation, ensuring that the key K stored in the blockchain AB Matching its hash value ensures that the key has not been tampered with.

[0159] Finally, based on security policies, keys are updated regularly or revoked when security incidents are detected, as shown below:

[0160] U(k)=updateorrevoke(K AB,newkey) (17)

[0161] Among them, U(k) represents the update or revocation operation, which generates a new key new_key and updates the blockchain record as needed.

[0162] In step S6, the machine learning model and encryption algorithm library are regularly updated to evaluate the security of each component and the overall operating efficiency of the system.

[0163] Specifically, a performance monitoring module is integrated into the system to collect key performance indicators in real time, such as encryption processing time, system response time, key generation and distribution efficiency, etc., as shown below:

[0164]

[0165] Where P(t) represents the average processing performance at time t, t i is the processing time of the ith operation, and n is the number of operations in the measurement period.

[0166] Based on the performance data, regularly adjust system parameters, such as the operating parameters of the encryption algorithm, network configuration, load balancing strategy, etc., to improve the overall performance and responsiveness of the system, as shown below:

[0167] Θ new =Θ old +ηΔP (19)

[0168] Among them, Θ new and θ old are the new and old parameter values, respectively, η is the adjustment step size (learning rate), and ΔP is the performance change, indicating the direction and size of the adjustment.

[0169] Under some examples, a regular update strategy including software patches, security updates, and hardware upgrades is implemented to adapt to new security threats and technological advances, as shown below:

[0170] U(d)=updateschedule(d, changes) (20)

[0171] where U(d) is the update operation defined at date d and changes is the list of requirements and security vulnerabilities accumulated since the last update.

[0172] Under some possible design ideas, a feedback mechanism is established to collect feedback from users and system operators, and analyze these data to continuously improve system design and operation, as shown below:

[0173] F(c)=feedbackanalysis(c) (21)

[0174] Among them, F(c) is the analysis result of the feedback content c, which is used to guide future system optimization and decision-making.

[0175] In step S7, a regular security audit program is implemented, including automated vulnerability scanning and manual security checks, to identify and evaluate potential security vulnerabilities and configurations that do not comply with policies, as shown below:

[0176]

[0177] Among them, A i represents the total risk score of the ith audit, a ij is the risk score of single examination j, and m is the total number of examination items.

[0178] At the same time, configure the system performance monitoring tool to collect key performance indicators in real time, such as response time, processing capacity, and system resource utilization, as shown below:

[0179]

[0180] Where P(t) is the average performance index at time t, x k (t) is the observed value of the kth performance indicator at time t, and n is the number of monitored performance indicators.

[0181] Under some possible design ideas, based on the results of audit and performance evaluation, improvement measures are developed, including adjusting configuration, updating software, optimizing algorithms, etc., as shown below:

[0182] C new =C old +β·ΔR (24)

[0183] Among them, C ncw and C old are the new and old configuration parameters, β is the adjustment coefficient, and ΔR is the item that needs to be improved based on the feedback.

[0184] Finally, a detailed audit and performance evaluation report is generated for management review, and the security strategy and performance optimization strategy are updated accordingly, as shown below:

[0185] S update (R)=polictadjust(R) (25)

[0186] Among them, S update (R) is the policy function updated based on the report R, which is used to adjust the security and performance strategy of the system.

[0187] The present invention provides continuous protection and optimization for the intelligent encrypted communication system between wireless routers through systematic security auditing and performance evaluation. Through regular auditing and real-time performance monitoring, the system can promptly discover and solve security vulnerabilities, performance bottlenecks and resource utilization problems, thereby keeping the system in the best state. In addition, the cycle of effect feedback and improvement measures ensures that the system constantly adapts to new security threats and technical challenges, and continuously improves user experience and system security.

[0188] The embodiment of the present invention further provides an intelligent encrypted communication system for wireless routers, the system comprising:

[0189] Initialization module 101 is used to deploy multi-point bandwidth sensors at key network nodes, initialize the network environment, start the wireless router, use advanced network scanning tools to collect network parameters, analyze the parameters in combination with real-time security monitoring and historical data, and use dynamic window technology to count and evaluate security events;

[0190] The model building module 102 is used to build a dynamic decision model based on the multi-armed bandit algorithm according to network parameters. At the same time, the dynamic decision model dynamically selects an encryption algorithm according to changes in the network environment, as shown below:

[0191]

[0192] Where A(t) represents the encryption algorithm selected at time t, C b (a,B(t)),C n (a,N(t)) and C e (a, E(t, T)) represent the cost functions of encryption algorithm a under the current bandwidth, number of device connections, and security event status, respectively. λ1, λ2, and λ3 represent weight factors used to balance different costs.

[0193] The encryption module 103 is used to equip the wireless router with a quantum light source and a quantum state detector according to the encryption algorithm to generate quantum bits and adjust the code, and optimize the wireless quantum channel by using optical and quantum isolation technology, and perform error rate testing and privacy amplification on the quantum bits to achieve security reinforcement; wherein the quantum state is defined as:

[0194]

[0195] Among them, |Φ|> represents the quantum state;

[0196] The encryption communication module 104 is used to monitor the encrypted communication in real time according to the communication flow after the quantum encryption is started, deploy an anomaly detection model based on deep learning to calculate the security score, and adaptively adjust the corresponding response according to the security score; when the anomaly detection model detects an anomaly, a deep behavior analysis and anomaly detection model is constructed to identify behaviors that deviate from the normal behavior pattern, and an adaptive response mechanism is designed according to the results of the deep behavior analysis and anomaly detection model to perform dynamic threshold management;

[0197] Building a blockchain network module 105, which is used to establish a blockchain network between routers, generate keys using a quantum key distribution system, and then exchange the keys to the blockchain network through a secure communication channel, store the generated keys in an encrypted form in the blockchain, and periodically update the keys based on security policies or revoke the keys when a security incident is detected;

[0198] Performance detection module 106, used to collect key performance indicators in real time to calculate processing performance, regularly adjust system parameters according to processing performance, and implement a regular update strategy and feedback mechanism to update and optimize the system;

[0199] The audit and evaluation module 107 is used to implement regular security audit procedures and configure system performance monitoring tools, develop improvement measures and generate audit and performance evaluation reports based on the results of security audits and performance monitoring.

[0200] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0201] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a division of logical functions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0202] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0203] Although embodiments of the present invention have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. An intelligent encryption communication method for wireless routers, characterized in that: The method is applied to an intelligent encryption communication system, and the method comprises: Deploy multi-point bandwidth sensors at key network nodes, initialize the network environment, start the wireless router, use advanced network scanning tools to collect network parameters, analyze the parameters in combination with real-time security monitoring and historical data, and use dynamic window technology to count and evaluate security events; A dynamic decision model based on a multi-armed bandit algorithm is constructed according to network parameters. At the same time, the dynamic decision model dynamically selects an encryption algorithm according to changes in the network environment, as shown below: Where A(t) represents the encryption algorithm selected at time t, C b (a,B(t)),C n (a,N(t)) and C e (a, E(t, T)) represent the cost functions of encryption algorithm a under the current bandwidth, number of device connections, and security event status, respectively. λ1, λ2, and λ3 represent weight factors used to balance different costs. According to the encryption algorithm, the wireless router is equipped with a quantum light source and a quantum state detector to generate quantum bits and adjust the code. At the same time, optical and quantum isolation technologies are used to optimize the wireless quantum channel, and error rate tests and privacy amplification of quantum bits are performed to achieve security reinforcement. Among them, the quantum state is defined as: Among them, |Φ|> represents the quantum state; Monitor encrypted communications in real time based on the communication flow after quantum encryption is activated, deploy an anomaly detection model based on deep learning to calculate the security score, and adaptively adjust the corresponding response based on the security score; when the anomaly detection model detects an anomaly, build a deep behavior analysis and anomaly detection model to identify behaviors that deviate from normal behavior patterns, and design an adaptive response mechanism based on the results of the deep behavior analysis and anomaly detection model to perform dynamic threshold management; Establish a blockchain network between routers, use the quantum key distribution system to generate keys, and then exchange the keys to the blockchain network through a secure communication channel. Store the generated keys in encrypted form in the blockchain, and update them regularly based on security policies or revoke them when security incidents are detected. Build a performance detection module to collect key performance indicators in real time to calculate processing performance, regularly adjust system parameters based on processing performance, and implement a regular update strategy and feedback mechanism to update and optimize the system; Implement regular security audit procedures and configure system performance monitoring tools, develop improvement measures and generate audit and performance evaluation reports based on the results of security audits and performance monitoring.

2. According to claim 1, a method for intelligent encrypted communication between wireless routers, characterized in that: The dynamic window technology is used to count and evaluate security events, as shown below: Among them, α(τ, t) represents a time decay function, which is used to weigh the timeliness of events; E(t, T) represents the total number of weighted security events in the past T time period before the deadline t; and e(τ) represents the number of security events that occurred at time τ.

3. According to claim 1, a method for intelligent encrypted communication between wireless routers, characterized in that: The wireless quantum channel is optimized by using optical and quantum isolation technology, and the transmission efficiency is expressed as: the=e -αLγ(E) Among them, η represents the quantum transmission efficiency, α represents the environmental attenuation coefficient, L represents the transmission distance, and γ(E) represents the additional loss caused by environmental noise; A highly sensitive single-photon detector is used to perform quantum measurement in a wireless communication environment, where the key M i The extraction is represented as: M i =|i><i|,i∈{0,1} Where i represents the calculation basis.

4. The intelligent encryption communication method for wireless routers according to claim 3, characterized in that: The error rate test and privacy amplification of quantum bits are performed to achieve security reinforcement, and the error correction is expressed as: Among them, P crror represents the key error rate, k i and k' i They represent the key bits obtained by the two routers from quantum measurement, and N represents the total number of bits.

5. The intelligent encryption communication method for wireless routers according to claim 1, characterized in that: The safety score is expressed as: S=f(v) Among them, S is the security score of the traffic, f is the function of the deep learning model, and v represents the features extracted from the traffic.

6. The intelligent encryption communication method for wireless routers according to claim 5, characterized in that: The detection formula of the deep behavior analysis and anomaly detection model is expressed as follows: Where D(t) represents the anomaly score at time t, h j represents the feature extraction function, v t represents the feature vector at time t, w j represents the weight parameter, θ represents the dynamically adjusted decision threshold, and σ represents the activation function; The adaptive response mechanism is expressed as follows: Among them, δ1 and δ2 represent the response thresholds adjusted according to the network status and security requirements; The adjustment of the dynamic threshold management is expressed as follows: Among them, α represents the learning rate, τ represents the target anomaly detection rate, and T represents the time window considered.

7. The intelligent encryption communication method for wireless routers according to claim 1, characterized in that: The blockchain network construction is shown as follows: B i =hash(B i-1 +T i +S i-1 ) Among them, B i is the i-th block, B i-1 is the previous block, T i is the key exchange transaction included in the current block, S i-1 is the state of the previous block; The key is generated using the quantum key distribution system, and then exchanged to the blockchain network through a secure communication channel, expressed as: K AB =QKD(A,B) Among them, K AB represents the key generated by routers A and B through quantum key distribution technology; The generated key is stored in the blockchain in an encrypted form. When the key is used, the system will verify the key, which is expressed as: V(k)=verify(K AB ,hash(K AB )) Among them, V(k) represents the verification operation, ensuring that the key K stored in the blockchain AB Matches its hash value; Based on security policies, keys are updated regularly or revoked when security incidents are detected, expressed as: U(k)=updateorrevoke(K AB ,newkey) Among them, U(k) represents the update or revocation operation, which generates a new key newkey and updates the blockchain record as needed.

8. The intelligent encryption communication method for wireless routers according to claim 1, characterized in that: In the performance detection module, the formula for calculating the processing performance is as follows: Where P(t) represents the average processing performance at time t, t i represents the processing time of the ith operation, and n represents the number of operations in the measurement period; Regularly adjust system parameters according to performance indicators, expressed as: I new =Θ old +ηΔP Among them, Θ new and θ old Represent the new and old parameter values ​​respectively, η represents the adjustment step size, and ΔP represents the performance change.

9. The intelligent encryption communication method for wireless routers according to claim 1, characterized in that: The security audit procedure is expressed as: Among them, A i represents the total risk score of the ith audit, a ij represents the risk score of a single inspection item j, and m represents the total number of inspection items; The calculation of the configuration system performance monitoring tool is expressed as: Among them, P(t) represents the average performance index at time t, x k (t) represents the observed value of the kth performance indicator at time t, and n represents the number of monitored performance indicators.

10. An intelligent encrypted communication system for wireless routers, characterized in that: The system comprises: Initialization module, used to deploy multi-point bandwidth sensors at key network nodes, initialize the network environment, start the wireless router, use advanced network scanning tools to collect network parameters, analyze the parameters in combination with real-time security monitoring and historical data, and use dynamic window technology to count and evaluate security events; The model building module is used to build a dynamic decision model based on the multi-armed bandit algorithm according to network parameters. At the same time, the dynamic decision model dynamically selects the encryption algorithm according to changes in the network environment, which is expressed as follows: Where A(t) represents the encryption algorithm selected at time t, C b (a,B(t)),C n (a,N(t)) and C e (a, E(t, T)) represent the cost functions of encryption algorithm a under the current bandwidth, number of device connections, and security event status, respectively. λ1, λ2, and λ3 represent weight factors used to balance different costs. The encryption module is used to equip the wireless router with a quantum light source and a quantum state detector to generate quantum bits and adjust the coding according to the encryption algorithm, and optimize the wireless quantum channel by using optical and quantum isolation technology, and perform error rate testing and privacy amplification on the quantum bits to achieve security reinforcement; wherein the quantum state is defined as: Among them, |Φ|> represents the quantum state; The encrypted communication module is used to monitor the encrypted communication in real time according to the communication flow after the quantum encryption is started, deploy an anomaly detection model based on deep learning to calculate the security score, and adaptively adjust the corresponding response according to the security score; when the anomaly detection model detects an anomaly, a deep behavior analysis and anomaly detection model is constructed to identify behaviors that deviate from the normal behavior pattern, and an adaptive response mechanism is designed according to the results of the deep behavior analysis and anomaly detection model to perform dynamic threshold management; Build a blockchain network module to establish a blockchain network between routers, generate keys using a quantum key distribution system, and then exchange the keys to the blockchain network through a secure communication channel. The generated keys are stored in an encrypted form in the blockchain, and the keys are regularly updated based on security policies or revoked when security incidents are detected; The performance detection module is used to collect key performance indicators in real time to calculate processing performance, regularly adjust system parameters based on processing performance, and implement a regular update strategy and feedback mechanism to update and optimize the system; the audit and evaluation module is used to implement regular security audit procedures and configure system performance monitoring tools, develop improvement measures and generate audit and performance evaluation reports based on the results of security audits and performance monitoring.

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