A cloud video conferencing system based on quantum key encryption

Abstract

The present invention discloses a cloud video conferencing system based on quantum key encryption, which relates to the technical field of quantum encryption. By real-time monitoring of quantum signal fluctuation information and quantum information identification information in the spatial channel of cloud video conference participants, after analysis, an attack risk coefficient Xgj is calculated and obtained, and compared with a preset risk threshold F to determine whether there is an eavesdropping attack risk. According to an anti-attack test instruction, an original loss information data set is constructed, and after introducing a quantum key update frequency, a verification loss information data set is constructed. An anti-attack test module calculates an attack risk fluctuation coefficient Xyg by comparing the difference between the two, and combines the attack risk coefficient Xgj to fit and generate an anti-attack test evaluation index Zcs, which is compared with an evaluation threshold G to determine whether the key update frequency is in a defensive equilibrium state, and make corresponding adjustments.

Description

Technical Field

[0001] The present invention relates to the field of quantum encryption technology, and in particular to a cloud video conferencing system based on quantum key encryption. Background Art

[0002] Quantum encryption technology is one of the most cutting-edge technologies in the field of modern communications. Its core lies in using the principles of quantum mechanics, especially quantum superposition and quantum entanglement, to encrypt and protect information. With the development of the information age, communication security issues have received increasing attention. Traditional encryption methods are facing more and more challenges, especially the rise of quantum computing, which poses a threat to existing encryption protocols. In this context, quantum key distribution, as an encryption method based on the principles of quantum physics, provides a secure communication method that is difficult to crack. Applying quantum key encryption technology to cloud video conferencing systems has become an important direction of current research and application. As the core platform for information exchange, the security of cloud video conferencing systems is directly related to the confidentiality and reliability of information transmission on a global scale. Therefore, the introduction of quantum key encryption technology provides a new protection mechanism for cloud video conferencing, which can ensure that the information shared by users during the meeting is not stolen or tampered with.

[0003] Although cloud video conferencing systems based on quantum key encryption have high theoretical security, they still face some significant challenges in practical applications. In particular, when facing actual network attacks and eavesdropping attacks, the existing system lacks an effective real-time attack identification and analysis mechanism, making it difficult to timely detect and evaluate changes in quantum signal loss and error rate, making it impossible to accurately determine whether it is in an attack state and conduct real-time risk warnings. In addition, there is a lack of effective defense measures to deal with eavesdropping attacks. To address these challenges, introducing quantum key update frequency is an effective defense strategy that can dynamically adjust the generation and exchange frequency of encryption keys to reduce the probability of attackers successfully stealing keys. However, too high or too low key update frequency will have negative effects. If the update frequency is too high, it will increase the quantum signal loss during signal transmission, thereby reducing the communication quality and encryption efficiency of the system; if the frequency is too low, the system will be in a long-term security risk, increasing the risk of being cracked by attackers. Therefore, there is an urgent need for a cloud video conferencing system based on quantum key encryption that integrates real-time monitoring, intelligent adjustment and resource optimization to improve the overall security, stability and operation efficiency of cloud video conferencing, and provide users with an efficient and highly secure cloud video conferencing environment. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a cloud video conferencing system based on quantum key encryption, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a cloud video conferencing system based on quantum key encryption, including a channel monitoring module, an attack risk analysis module, a data construction module, an anti-attack test module and a defense balancing module;

[0006] The channel monitoring module is used to monitor the quantum signal fluctuations in each spatial channel of the cloud video conference participants, summarize and obtain a quantum signal fluctuation information data set, and monitor the quantum bit reception of the receiving end and the sending end through a quantum key distribution controller and a quantum error rate monitoring sensor, and summarize and obtain a quantum information identification information data set;

[0007] The attack risk analysis module is used to analyze the quantum signal fluctuation information data set and the quantum signal identification information data set, and obtain the attack risk coefficient Xgj by fitting. By comparing and analyzing the attack risk coefficient Xgj with the preset risk threshold F, it is determined whether the current cloud video conference is in an unsafe eavesdropping attack state. If so, an anti-attack test instruction is issued externally;

[0008] The data construction module is used to monitor and construct the original loss information data set in the spatial channel of the current cloud video conference participant after receiving the anti-attack test instruction and before the verification key update frequency is introduced, establish a comparison benchmark, and construct the verification loss information data set after the quantum key update frequency is introduced;

[0009] The anti-attack test module is used to obtain the post-attack risk fluctuation coefficient Xyg according to the difference between the original loss information data set and the verification loss information data set, and combine it with the attack risk coefficient Xgj, and after machine deep learning and dimensionless processing, fit and obtain the anti-attack test evaluation index Zcs;

[0010] The defense balance module is used to pre-set the evaluation threshold G, and compare and analyze the anti-attack test evaluation index Zcs with the evaluation threshold G to comprehensively evaluate whether the currently introduced verification key update frequency is in a defense balance state, and take corresponding adjustment measures according to the corresponding balance status.

[0011] Preferably, the channel monitoring module includes a deployment unit, a first monitoring unit and a second monitoring unit;

[0012] The deployment unit is used to deploy several groups of components at the sending end and receiving end of the quantum communication link of the cloud video conferencing device, including quantum photon detectors, quantum key distribution controllers, quantum error rate monitoring sensors and optical power sensors.

[0013] Preferably, the first monitoring unit is used to use the deployed quantum photon detector and set the monitoring time period to continuously monitor the quantum signal fluctuations in each spatial channel of the cloud video conference participants, and summarize and obtain a quantum signal fluctuation information data set, wherein the quantum signal fluctuation information data set includes the received signal strength value Qqd of each spatial channel within the monitoring time period, and extracts the maximum value Qqd of the received signal strength of each spatial channel within the monitoring time period from the quantum signal fluctuation information data set. max And the minimum received signal strength Qqd min , and combined with the statistical averaging algorithm, obtain the maximum received signal strength mean of the spatial channel during the monitoring period , minimum received signal strength mean and the average received signal strength ;

[0014] The second monitoring unit is used to set a monitoring time period, monitor the quantum bit reception status of the receiving end and the transmitting end through the quantum key distribution controller and the quantum error rate monitoring sensor, and summarize and obtain a quantum information identification information data set, wherein the quantum information identification information data set includes the number of transmitted quantum bits Nfs and the number of received error quantum bits Ncw of each spatial channel in the monitoring time period, and combines the average value algorithm in statistics to obtain the average number of transmitted quantum bits of the spatial channel in the monitoring time period. and the average number of received error qubits .

[0015] Preferably, the attack risk analysis module includes a signal fluctuation analysis unit, a signal recognition analysis unit and an attack risk assessment unit;

[0016] The signal fluctuation analysis unit is used to analyze the quantum signal fluctuation information data set to obtain the quantum signal fluctuation coefficient Xbd of the spatial channel in the monitoring time period, and the quantum signal fluctuation coefficient Xbd is obtained by the following formula;

[0017] ;

[0018] In the formula, It is expressed as the mean value of the maximum received signal strength of the spatial channel during the monitoring period. It is expressed as the minimum mean received signal strength of the spatial channel during the monitoring period, It is expressed as the average received signal strength of the spatial channel during the monitoring period.

[0019] Preferably, the signal recognition and analysis unit is used to analyze the quantum information recognition information data set to obtain the quantum bit recognition error rate Vbt of the spatial channel in the monitoring time period, and the quantum bit recognition error rate Vbt is obtained by the following formula;

[0020] ;

[0021] In the formula, Expressed as the average number of sent qubits, Expressed as the average number of received error qubits.

[0022] Preferably, the attack risk assessment unit is used to associate the quantum signal fluctuation information data set with the quantum signal identification information data set, and after dimensionless processing, obtain the attack risk coefficient Xgj by fitting, and the attack risk coefficient Xgj is obtained by the following formula;

[0023] ;

[0024] Where Xbd represents the quantum signal fluctuation coefficient of the spatial channel during the monitoring period, Vbt represents the quantum bit recognition error rate of the spatial channel during the monitoring period, and are the weight values ​​of the quantum signal fluctuation coefficient Xbd and the quantum bit recognition error rate Vbt of the spatial channel in the monitoring time period respectively;

[0025] By comparing and analyzing the attack risk coefficient Xgj with the preset risk threshold F, it is determined whether the current cloud video conference is in an unsafe eavesdropping attack state. The specific process is as follows:

[0026] If the attack risk coefficient Xgj ≥ risk threshold F, it means that the current cloud video conference is in an unsafe eavesdropping attack state, and an anti-attack test instruction will be issued externally;

[0027] If the attack risk coefficient Xgj is less than the risk threshold F, it indicates that the currently ongoing cloud video conference is not in an unsafe eavesdropping attack state, and no additional anti-attack test instruction is issued at this time.

[0028] Preferably, the data construction module is used to construct an original loss information data set as a comparison benchmark after receiving an anti-attack test instruction and before introducing a verification key update frequency, based on monitoring the pre-introduction quantum signal transmission power Wr and the pre-introduction channel noise Wc in the spatial channel of the current cloud video conference participant; and to construct a verification loss information data set by monitoring the post-introduction quantum signal transmission power Wrr and the post-introduction channel noise Wcc in the spatial channel of the cloud video conference participant after introducing quantum key update frequencies of different intensities.

[0029] Preferably, the anti-attack test module includes a post-attack risk analysis unit and an anti-attack test evaluation unit;

[0030] The post-initiate attack risk analysis unit is used to obtain the post-initiate attack risk fluctuation coefficient Xyg after normalization according to the difference between the original loss information data set and the verification loss information data set. The post-initiate attack risk fluctuation coefficient Xyg is obtained by the following formula;

[0031] ;

[0032] Where Wr represents the quantum signal transmission power before introduction, Wc represents the channel noise before introduction, Wrr represents the quantum signal transmission power after introduction, and Wcc represents the channel noise after introduction. and are all expressed as weight values.

[0033] Preferably, the anti-attack test evaluation unit is used to combine the acquired post-attack risk fluctuation coefficient Xyg with the attack risk coefficient Xgj, and after machine deep learning and dimensionless processing, fit to obtain the anti-attack test evaluation index Zcs, and the anti-attack test evaluation index Zcs is obtained by the following formula;

[0034] ;

[0035] In the formula, F represents the preset risk threshold, and Both are expressed as weight values, and A is expressed as a correction constant.

[0036] Preferably, the defense balance module is used to pre-set an evaluation threshold G, and compare and analyze the anti-attack test evaluation index Zcs with the evaluation threshold G to comprehensively evaluate whether the currently introduced verification key update frequency is in a defense balance state. The specific process is as follows;

[0037] If the anti-attack test evaluation index Zcs> the evaluation threshold G, it means that the currently introduced verification key update frequency is not in a defensive equilibrium state. At this time, the verification key update frequency will be enhanced, and the enhanced verification key update frequency will continue to be introduced until it is in a defensive equilibrium state to cope with the current eavesdropping attack behavior and meet the security and confidentiality requirements of cloud video conferencing;

[0038] If the anti-attack test evaluation index Zcs = the evaluation threshold G, it means that the currently introduced verification key update frequency is in a defensive equilibrium state, meets the current cloud video conferencing security and confidentiality requirements, and maintains the current verification key update frequency, while continuing to implement the channel monitoring module to monitor the attack behavior;

[0039] If the anti-attack test evaluation index Zcs is less than the evaluation threshold G, it means that the currently introduced verification key update frequency is not in a defensive equilibrium state. At this time, the verification key update frequency will be weakened, and the weakened verification key update frequency will continue to be introduced until it is in a defensive equilibrium state to deal with the current eavesdropping attack behavior and meet the security and confidentiality requirements of cloud video conferencing.

[0040] The present invention provides a cloud video conferencing system based on quantum key encryption, which has the following beneficial effects:

[0041] (1) A cloud video conferencing system is designed based on quantum key encryption technology, which has the significant advantages of real-time monitoring, dynamic defense, and resource balancing. First, through the channel monitoring module and the attack risk analysis module, the quantum signal fluctuation and quantum information recognition can be accurately monitored, and the potential eavesdropping attack risks can be quickly identified, providing real-time security feedback for the system. On this basis, the data construction module and the anti-attack test module ensure that the system improves its anti-attack capability through difference analysis and deep learning algorithms before and after the introduction of the verification key update frequency, so that the system can adapt to the complex channel environment and dynamically adjust the key update frequency, thereby significantly enhancing security. At the same time, the defense balance module realizes the intelligent adjustment of the key update frequency. The system can avoid excessive resource consumption while ensuring security protection, and ensure the balance between defense strategy and resource utilization. This cloud video conferencing system that integrates real-time monitoring, intelligent adjustment and resource optimization improves the overall security, stability and operation efficiency, and provides users with an efficient and highly secure cloud video conferencing environment.

[0042] (2) The channel monitoring module realizes real-time monitoring of quantum signal fluctuations, which can effectively monitor the quantum signal changes in the spatial channel where each participant in the cloud video conference is located. Combined with the quantum key distribution controller and the quantum error rate monitoring sensor, it can obtain the quantum bit reception data of the receiving and sending ends, thereby accurately summarizing the quantum signal fluctuation data set and the quantum information identification data set. The attack risk analysis module further analyzes these data, calculates the attack risk coefficient Xgj, and compares it with the preset risk threshold F. It can quickly identify and judge whether the cloud video conferencing system is at risk of eavesdropping attack. Once the risk threshold is exceeded, the system immediately issues an anti-attack test instruction, which effectively improves the sensitivity to potential security threats and enables the system to detect anomalies at the beginning of the attack. This real-time monitoring and accurate identification function is particularly critical in the face of complex quantum channel environments. It helps to identify and respond to potential attacks before they cause substantial damage to the system, greatly improving the security of the cloud video conferencing system.

[0043] (3) By introducing the verification key update frequency to enhance the anti-attack capability, the data construction module first constructs the original loss information data set before the verification key update frequency is introduced as a comparison benchmark; after the key update frequency is introduced, the verification loss information data set is constructed. The anti-attack test module calculates the post-introduction attack risk fluctuation coefficient Xyg by comparing the difference between the two, and combines it with the original attack risk coefficient Xgj, and then generates the anti-attack test evaluation index Zcs through deep learning algorithm and dimensionless processing technology. This dynamic adjustment mechanism enables the system to adjust the key update frequency adaptively in the later stage according to the quantum signal fluctuation and attack risk changes in the current channel environment, thereby effectively improving the system's anti-attack capability. In this way, it can not only quickly identify and defend against eavesdropping attacks, but also ensure communication security and data integrity under complex channel conditions, providing higher security protection for cloud video conferencing systems;

[0044] At the same time, based on the anti-attack test evaluation index Zcs, a comparison analysis is performed with the preset evaluation threshold G to determine whether the current key update frequency has reached a defense balance state. If the anti-attack test evaluation index Zcs is higher or lower than the evaluation threshold G, the system will correspondingly increase or decrease the verification key update frequency to dynamically adapt to the current eavesdropping attack risk. On the premise that the system meets security requirements, the defense balance module can effectively avoid the additional computing and communication resource consumption caused by frequent key updates, reduce system load, and ensure that cloud video conferencing can be in a balance between security and performance. This mechanism not only improves the resource utilization efficiency of the system, but also enhances the overall stability of cloud video conferencing, which helps to ensure the smoothness of user experience and efficient operation of the system while responding to high-frequency attacks. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a block diagram of a cloud video conferencing system based on quantum key encryption in the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Example 1

[0048] See also Figure 1 , the present invention provides a cloud video conferencing system based on quantum key encryption, including a channel monitoring module, an attack risk analysis module, a data construction module, an anti-attack test module and a defense balancing module;

[0049] The channel monitoring module is used to monitor the quantum signal fluctuations in each spatial channel of the cloud video conference participants, summarize and obtain a quantum signal fluctuation information data set, and monitor the quantum bit reception of the receiving end and the sending end through a quantum key distribution controller and a quantum error rate monitoring sensor, and summarize and obtain a quantum information identification information data set;

[0050] The attack risk analysis module is used to analyze the quantum signal fluctuation information data set and the quantum signal identification information data set, and obtain the attack risk coefficient Xgj by fitting. By comparing and analyzing the attack risk coefficient Xgj with the preset risk threshold F, it is determined whether the current cloud video conference is in an unsafe eavesdropping attack state. If so, an anti-attack test instruction is issued externally;

[0051] The data construction module is used to monitor and construct the original loss information data set in the spatial channel of the current cloud video conference participant after receiving the anti-attack test instruction and before the verification key update frequency is introduced, establish a comparison benchmark, and construct the verification loss information data set after the quantum key update frequency is introduced;

[0052] The anti-attack test module is used to obtain the post-attack risk fluctuation coefficient Xyg according to the difference between the original loss information data set and the verification loss information data set, and combine it with the attack risk coefficient Xgj, and after machine deep learning and dimensionless processing, fit and obtain the anti-attack test evaluation index Zcs;

[0053] The defense balance module is used to pre-set the evaluation threshold G, and compare and analyze the anti-attack test evaluation index Zcs with the evaluation threshold G to comprehensively evaluate whether the currently introduced verification key update frequency is in a defense balance state, and take corresponding adjustment measures according to the corresponding balance status.

[0054] In this embodiment, a highly secure cloud video conferencing system is provided by quantum key encryption, which has the significant advantages of real-time monitoring, dynamic defense and resource balancing. The channel monitoring module can track the fluctuation of quantum signals in real time in complex spatial channels, and accurately collect quantum information identification data to provide timely attack risk assessment for the system. The attack risk analysis module accurately determines whether the cloud video conference is in an eavesdropping attack state through in-depth analysis of the quantum signal fluctuation information data set and the quantum information identification information data set, and issues an anti-attack test instruction to ensure that the system starts protective measures in a timely manner. The data construction module collects loss information under different states of not introducing and introducing the verification key update frequency, constructs a comparison data set, and provides an accurate loss assessment basis, so that the anti-attack test module can accurately identify the post-attack risk fluctuation coefficient Xyg of the attack behavior, and use machine learning to fit the system's anti-attack test evaluation index Zcs, thereby significantly improving the system's ability to respond to attacks. In addition, the defense balancing module ensures that the system achieves the best balance between key update frequency and resource consumption without sacrificing security, optimizes the overall defense state of the system, and enables the cloud video conferencing system to reduce unnecessary resource consumption while operating efficiently and safely, ensuring stability and user experience.

[0055] Example 2

[0056] Please refer to Figure 1 ,Specifically: the channel monitoring module includes a deployment unit, a first monitoring unit and a second monitoring unit;

[0057] The deployment unit is used to deploy several groups of components at the sending end and receiving end of the quantum communication link of the cloud video conferencing device, including quantum photon detectors, quantum key distribution controllers, quantum error rate monitoring sensors and optical power sensors.

[0058] The first monitoring unit is used to use the deployed quantum photon detector and set the monitoring time period to continuously monitor the quantum signal fluctuations in each spatial channel of the cloud video conference participants, and summarize and obtain a quantum signal fluctuation information data set, wherein the quantum signal fluctuation information data set includes a received signal strength value Qqd of each spatial channel within the monitoring time period, and extracts the maximum value Qqd of the received signal strength of each spatial channel within the monitoring time period from the quantum signal fluctuation information data set. max And the minimum received signal strength Qqd min , and combined with the statistical averaging algorithm, obtain the maximum received signal strength mean of the spatial channel during the monitoring period , minimum received signal strength mean and the average received signal strength ;

[0059] The second monitoring unit is used to set a monitoring time period, monitor the quantum bit reception status of the receiving end and the transmitting end through the quantum key distribution controller and the quantum error rate monitoring sensor, and summarize and obtain a quantum information identification information data set, wherein the quantum information identification information data set includes the number of transmitted quantum bits Nfs and the number of received error quantum bits Ncw of each spatial channel in the monitoring time period, and combines the average value algorithm in statistics to obtain the average number of transmitted quantum bits of the spatial channel in the monitoring time period. and the average number of received error qubits .

[0060] It should be noted that the fluctuation of quantum signals in the spatial channel can reflect whether it is subject to eavesdropping attacks, because the basic principles of quantum mechanics determine that once the quantum state is measured, it will undergo irreversible changes. This means that any measurement of quantum signals, such as eavesdropping, will change the originally stable state of quantum signals. These changes will cause the volatility of quantum signals to increase. Therefore, by monitoring the fluctuations of quantum signals in the spatial channel, the system can capture signs of existing attacks. Specifically, when the fluctuation of signal strength is within a stable range under normal circumstances, obvious abnormal characteristics of signal strength fluctuations will appear during eavesdropping, indicating that there is interference from eavesdropping attacks on the transmission link.

[0061] In addition, the quantum information identification situation is monitored in real time through the quantum key distribution controller and the quantum error rate monitoring sensor, which can record the quantum bit matching between the sender and the receiver. If the system is in a normal communication state, the bit reception situation at the receiver and the sender should maintain a low error rate. However, when eavesdropping occurs, the attacker's measurement behavior will introduce quantum state errors, causing the bit error rate at the receiver to increase. This is because eavesdropping will destroy the original quantum superposition state or entangled state, affecting the accuracy of bit identification, thereby increasing the probability of receiving bit errors. By summarizing the quantum bit reception situation and analyzing the error rate data, the system can further confirm whether there is eavesdropping and use this as a basis for judging the channel security.

[0062] In this embodiment, the transmission stability and security of quantum signals in the cloud video conferencing system are ensured by deploying a deployment unit, a first monitoring unit, and a second monitoring unit. The system can obtain accurate signal data at key positions of the channel by deploying quantum photon detectors, quantum key distribution controllers, quantum error rate monitoring sensors, and optical power sensors at the transmitting and receiving ends of the quantum communication link. The first monitoring unit uses a quantum photon detector to monitor the quantum signal fluctuations in each spatial channel in real time, extracts the intensity extreme value of the received signal, and calculates its mean and average signal strength through a mean algorithm, which can more effectively capture the abnormal fluctuation characteristics of the channel. The second monitoring unit uses a quantum key distribution controller and an error rate monitoring sensor to carefully track the quantum bit reception at the transmitting and receiving ends, further summarizes the number of transmitted quantum bits and the number of received error quantum bits of each channel, and calculates the average value to evaluate the quality and security of the channel. Overall, the channel monitoring module not only enhances the stability detection of quantum signals, but also can promptly identify the existence of channel interference or signs of attack, providing a more reliable and sophisticated security guarantee for cloud video conferencing.

[0063] Example 3

[0064] Please refer to Figure 1 ,Specifically: the attack risk analysis module includes a signal fluctuation analysis unit, a signal recognition analysis unit and an attack risk assessment unit;

[0065] The signal fluctuation analysis unit is used to analyze the quantum signal fluctuation information data set to obtain the quantum signal fluctuation coefficient Xbd of the spatial channel in the monitoring time period, and the quantum signal fluctuation coefficient Xbd is obtained by the following formula;

[0066] ;

[0067] In the formula, It is expressed as the mean value of the maximum received signal strength of the spatial channel during the monitoring period. It is expressed as the minimum mean received signal strength of the spatial channel during the monitoring period, It is expressed as the average received signal strength of the spatial channel during the monitoring period.

[0068] Specifically, the signal recognition and analysis unit is used to analyze the quantum information recognition information data set to obtain the quantum bit recognition error rate Vbt of the spatial channel in the monitoring time period, and the quantum bit recognition error rate Vbt is obtained by the following formula;

[0069] ;

[0070] In the formula, Expressed as the average number of sent qubits, Expressed as the average number of received error qubits.

[0071] Specifically, the attack risk assessment unit is used to associate the quantum signal fluctuation information data set with the quantum signal identification information data set, and after dimensionless processing, obtain the attack risk coefficient Xgj by fitting. The attack risk coefficient Xgj is obtained by the following formula:

[0072] ;

[0073] Where Xbd represents the quantum signal fluctuation coefficient of the spatial channel during the monitoring period, Vbt represents the quantum bit recognition error rate of the spatial channel during the monitoring period, and are the weight values ​​of the quantum signal fluctuation coefficient Xbd and the quantum bit recognition error rate Vbt of the spatial channel in the monitoring time period respectively;

[0074] By comparing and analyzing the attack risk coefficient Xgj with the preset risk threshold F, it is determined whether the current cloud video conference is in an unsafe eavesdropping attack state. The specific process is as follows:

[0075] If the attack risk coefficient Xgj ≥ risk threshold F, it means that the current cloud video conference is in an unsafe eavesdropping attack state, and an anti-attack test instruction will be issued externally;

[0076] If the attack risk coefficient Xgj is less than the risk threshold F, it indicates that the currently ongoing cloud video conference is not in an unsafe eavesdropping attack state, and no additional anti-attack test instruction is issued at this time.

[0077] In this embodiment, the detection and early warning capabilities of eavesdropping attacks are improved through the signal fluctuation analysis unit, the signal identification analysis unit and the attack risk assessment unit. The signal fluctuation analysis unit calculates the quantum signal fluctuation coefficient Xbd by analyzing the quantum signal fluctuation information data set, thereby evaluating the fluctuation degree of the quantum signal in the channel in real time and identifying abnormal signal fluctuations. The signal identification analysis unit further calculates the quantum bit recognition error rate Vbt, and identifies potential attack risks by analyzing the difference between the quantum bit receiving and sending conditions. Finally, the attack risk assessment unit performs dimensionless processing and fitting on the quantum signal fluctuation coefficient Xbd and the quantum bit recognition error rate Vbt to generate an attack risk coefficient Xgj, and compares it with a preset risk threshold F to determine whether the cloud video conference is in an unsafe state. The advantage of this analysis process is that a more accurate risk assessment is achieved by integrating the dual data sources of signal fluctuations and quantum bit recognition conditions, and anti-attack test instructions can be triggered according to the real-time risk coefficient, making the system more sensitive and efficient in dealing with complex eavesdropping attacks, further ensuring the confidentiality of the conference content and the reliability of transmission.

[0078] Example 4

[0079] Please refer to Figure 1 Specifically: the data construction module is used to construct an original loss information data set as a comparison benchmark after receiving an anti-attack test instruction and before introducing a verification key update frequency, based on monitoring the pre-introduction quantum signal transmission power Wr and the pre-introduction channel noise Wc in the spatial channel of the current cloud video conference participant; after introducing quantum key update frequencies of different intensities, the post-introduction quantum signal transmission power Wrr and the post-introduction channel noise Wcc in the spatial channel of the cloud video conference participant are monitored to construct a verification loss information data set.

[0080] It should be noted that changing the verification key update frequency can effectively defend against eavesdropping attacks, because a higher key update frequency shortens the effective use time of each key, thereby reducing the difficulty and value of attackers stealing and cracking keys. In the quantum key distribution system, eavesdroppers need enough time to intercept, measure and analyze quantum states in an attempt to obtain useful information from them. However, when the key update frequency increases, the keys are replaced quickly. Even if the attacker successfully steals a key fragment, it will quickly become invalid and cannot pose a real threat to the entire communication. This frequent key update forces the attacker to constantly restart the eavesdropping process, which increases the difficulty of cracking and resource consumption, and effectively improves the system's anti-attack capabilities. At the same time, if the update frequency can be dynamically adjusted according to the channel security status, the system can further optimize the defense effect and ensure both safe and efficient key management.

[0081] In this embodiment, by collecting quantum signal transmission power and channel noise, a loss data set before and after the introduction of the verification key update frequency is constructed, providing a clear comparison benchmark. This design allows the system to monitor the changes in quantum signal transmission loss under different key update frequencies, and further analyze the channel security status. By constructing the original loss information data set and the verification loss information data set, the system can intuitively evaluate the security changes after the introduction of the key update frequency, thereby more accurately judging the impact of the key update frequency on the channel loss and anti-attack effect. The advantage of this process is that it provides a systematic comparison benchmark and verification means, so that the system can dynamically adjust the key update frequency to achieve the optimal balance, which not only improves the anti-attack capability, but also avoids unnecessary waste of resources, and achieves the simultaneous optimization of security and performance.

[0082] Example 5

[0083] Please refer to Figure 1 ,Specifically: the anti-attack test module includes a post-attack risk analysis unit and an anti-attack test evaluation unit;

[0084] The post-initiate attack risk analysis unit is used to obtain the post-initiate attack risk fluctuation coefficient Xyg after normalization according to the difference between the original loss information data set and the verification loss information data set. The post-initiate attack risk fluctuation coefficient Xyg is obtained by the following formula;

[0085] ;

[0086] Where Wr represents the quantum signal transmission power before introduction, Wc represents the channel noise before introduction, Wrr represents the quantum signal transmission power after introduction, and Wcc represents the channel noise after introduction. and are all expressed as weight values.

[0087] Specifically, the anti-attack test evaluation unit is used to combine the acquired post-attack risk fluctuation coefficient Xyg with the attack risk coefficient Xgj, and after machine deep learning and dimensionless processing, fit to obtain the anti-attack test evaluation index Zcs, and the anti-attack test evaluation index Zcs is obtained by the following formula;

[0088] ;

[0089] In the formula, F represents the preset risk threshold, and Both are expressed as weight values, and A is expressed as a correction constant.

[0090] In this embodiment, an accurate anti-attack capability assessment means is provided through a post-attack risk analysis unit and an anti-attack test evaluation unit. The post-attack risk analysis unit calculates the post-attack risk fluctuation coefficient Xyg based on the difference between the original loss information data set and the verification loss information data set after normalization processing, thereby accurately reflecting the change in the channel security status before and after the key update. The anti-attack test evaluation unit further combines the post-attack risk fluctuation coefficient Xyg with the attack risk coefficient Xgj, and generates an anti-attack test evaluation index Zcs through machine deep learning and dimensionless processing fitting. The anti-attack test evaluation index Zcs can not only comprehensively evaluate the effect of the current key update frequency in resisting attacks, but also provide accurate anti-attack capability quantification results, providing a scientific basis for the system to dynamically adjust the defense strategy under different conditions. The significant advantage of this process is that, through a comprehensive evaluation system, the key update frequency adjustment process is optimized, the anti-attack capability is maximized, and the system resource consumption and security are effectively balanced, providing strong support for the efficient and secure operation of the cloud video conferencing system.

[0091] Example 6

[0092] Please refer to Figure 1 Specifically: the defense balance module is used to pre-set the evaluation threshold G, and compare and analyze the anti-attack test evaluation index Zcs with the evaluation threshold G to comprehensively evaluate whether the currently introduced verification key update frequency is in a defense balance state. The specific process is as follows;

[0093] If the anti-attack test evaluation index Zcs> the evaluation threshold G, it means that the currently introduced verification key update frequency is not in a defensive equilibrium state. At this time, the verification key update frequency will be enhanced, and the enhanced verification key update frequency will continue to be introduced until it is in a defensive equilibrium state to cope with the current eavesdropping attack behavior and meet the security and confidentiality requirements of cloud video conferencing;

[0094] If the anti-attack test evaluation index Zcs = the evaluation threshold G, it means that the currently introduced verification key update frequency is in a defensive equilibrium state, meets the current cloud video conferencing security and confidentiality requirements, and maintains the current verification key update frequency, while continuing to implement the channel monitoring module to monitor the attack behavior;

[0095] If the anti-attack test evaluation index Zcs is less than the evaluation threshold G, it means that the currently introduced verification key update frequency is not in a defensive equilibrium state. At this time, the verification key update frequency will be weakened, and the weakened verification key update frequency will continue to be introduced until it is in a defensive equilibrium state to deal with the current eavesdropping attack behavior and meet the security and confidentiality requirements of cloud video conferencing.

[0096] In this embodiment, by presetting an evaluation threshold G and comparing and analyzing the anti-attack test evaluation index Zcs with the preset evaluation threshold G, dynamic adjustment of the key update frequency and defense balance are achieved. According to the evaluation result, it is determined in real time whether the current verification key update frequency is in a defense balance state. Specifically, if the anti-attack test evaluation index Zcs is higher than the evaluation threshold G, it means that the current key update frequency is insufficient to cope with the current attack risk. The system will automatically enhance the key update frequency to improve security and prevent potential eavesdropping. When the anti-attack test evaluation index Zcs is equal to the evaluation threshold G, it indicates that the current frequency has reached a defense balance state and the security and resource utilization have reached an optimal balance. At this time, the current frequency will be maintained, and the security status of the system will continue to be monitored through the channel monitoring module to ensure that the meeting is always in a protected environment. The attack test evaluation index Zcs is lower than the evaluation threshold G, indicating that the current update frequency is too high. The system will appropriately reduce the update frequency to avoid unnecessary resource consumption and improve resource utilization efficiency. The significant advantage of this design lies in its automated and intelligent adjustment mechanism, which can optimize the key update frequency according to the real-time status of the system, so that the cloud video conferencing system can achieve an ideal balance between security and performance. Through dynamic adjustment, the defense balancing module can ensure that sufficient protection can be provided in different attack environments, while avoiding excessive resource occupation and system load caused by excessive frequency, making it more flexible and efficient in dealing with complex and changeable attack scenarios, providing reliable protection for the security, reliability and efficiency of cloud video conferencing, thereby ensuring the confidentiality of meeting content and the stability of data transmission, and improving user experience and the continuity of system operation.

[0097] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art 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 appended claims and their equivalents.

Claims

1. A cloud video conferencing system based on quantum key encryption, characterized by: It includes channel monitoring module, attack risk analysis module, data construction module, anti-attack test module and defense balance module; The channel monitoring module is used to monitor the quantum signal fluctuations in each spatial channel of the cloud video conference participants, summarize and obtain a quantum signal fluctuation information data set, and monitor the quantum bit reception of the receiving end and the sending end through a quantum key distribution controller and a quantum error rate monitoring sensor, and summarize and obtain a quantum information identification information data set; The attack risk analysis module is used to analyze the quantum signal fluctuation information data set and the quantum signal identification information data set, and obtain the attack risk coefficient Xgj by fitting. By comparing and analyzing the attack risk coefficient Xgj with the preset risk threshold F, it is determined whether the current cloud video conference is in an unsafe eavesdropping attack state. If so, an anti-attack test instruction is issued externally; The data construction module is used to monitor and construct the original loss information data set in the spatial channel of the current cloud video conference participant after receiving the anti-attack test instruction and before the verification key update frequency is introduced, establish a comparison benchmark, and construct the verification loss information data set after the quantum key update frequency is introduced; The anti-attack test module is used to obtain the post-attack risk fluctuation coefficient Xyg according to the difference between the original loss information data set and the verification loss information data set, and combine it with the attack risk coefficient Xgj, and after machine deep learning and dimensionless processing, fit and obtain the anti-attack test evaluation index Zcs; The defense balance module is used to pre-set the evaluation threshold G, and compare and analyze the anti-attack test evaluation index Zcs with the evaluation threshold G to comprehensively evaluate whether the currently introduced verification key update frequency is in a defense balance state, and take corresponding adjustment measures according to the corresponding balance status.

2. A cloud video conferencing system based on quantum key encryption according to claim 1, characterized in that: The channel monitoring module includes a deployment unit, a first monitoring unit and a second monitoring unit; The deployment unit is used to deploy several groups of components at the sending end and receiving end of the quantum communication link of the cloud video conferencing device, including quantum photon detectors, quantum key distribution controllers, quantum error rate monitoring sensors and optical power sensors.

3. A cloud video conferencing system based on quantum key encryption according to claim 2, characterized in that: The first monitoring unit is used to use the deployed quantum photon detector and set the monitoring time period to continuously monitor the quantum signal fluctuations in each spatial channel of the cloud video conference participants, and summarize and obtain a quantum signal fluctuation information data set, wherein the quantum signal fluctuation information data set includes a received signal strength value Qqd of each spatial channel within the monitoring time period, and extracts the maximum value Qqd of the received signal strength of each spatial channel within the monitoring time period from the quantum signal fluctuation information data set. max And the minimum received signal strength Qqd min , and combined with the statistical averaging algorithm, obtain the maximum received signal strength mean of the spatial channel during the monitoring period , minimum received signal strength mean and the average received signal strength ; The second monitoring unit is used to set a monitoring time period, monitor the quantum bit reception status of the receiving end and the transmitting end through the quantum key distribution controller and the quantum error rate monitoring sensor, and summarize and obtain a quantum information identification information data set, wherein the quantum information identification information data set includes the number of transmitted quantum bits Nfs and the number of received error quantum bits Ncw of each spatial channel in the monitoring time period, and combines the average value algorithm in statistics to obtain the average number of transmitted quantum bits of the spatial channel in the monitoring time period. and the average number of received error qubits .

4. A cloud video conferencing system based on quantum key encryption according to claim 3, characterized in that: The attack risk analysis module includes a signal fluctuation analysis unit, a signal recognition analysis unit and an attack risk assessment unit; The signal fluctuation analysis unit is used to analyze the quantum signal fluctuation information data set to obtain the quantum signal fluctuation coefficient Xbd of the spatial channel in the monitoring time period, and the quantum signal fluctuation coefficient Xbd is obtained by the following formula; ； In the formula, It is expressed as the mean value of the maximum received signal strength of the spatial channel during the monitoring period. It is expressed as the minimum mean received signal strength of the spatial channel during the monitoring period, It is expressed as the average received signal strength of the spatial channel during the monitoring period.

5. A cloud video conferencing system based on quantum key encryption according to claim 4, characterized in that: The signal recognition and analysis unit is used to analyze the quantum information recognition information data set to obtain the quantum bit recognition error rate Vbt of the spatial channel in the monitoring time period, and the quantum bit recognition error rate Vbt is obtained by the following formula; ； In the formula, Expressed as the average number of sent qubits, Expressed as the average number of received error qubits.

6. A cloud video conferencing system based on quantum key encryption according to claim 4, characterized in that: The attack risk assessment unit is used to associate the quantum signal fluctuation information data set with the quantum signal identification information data set, and after dimensionless processing, fit to obtain the attack risk coefficient Xgj, and the attack risk coefficient Xgj is obtained by the following formula; ； Where Xbd represents the quantum signal fluctuation coefficient of the spatial channel during the monitoring period, Vbt represents the quantum bit recognition error rate of the spatial channel during the monitoring period, and are the weight values ​​of the quantum signal fluctuation coefficient Xbd and the quantum bit recognition error rate Vbt of the spatial channel in the monitoring time period respectively; By comparing and analyzing the attack risk coefficient Xgj with the preset risk threshold F, it is determined whether the current cloud video conference is in an unsafe eavesdropping attack state. The specific process is as follows: If the attack risk coefficient Xgj ≥ risk threshold F, it means that the current cloud video conference is in an unsafe eavesdropping attack state, and an anti-attack test instruction will be issued externally; If the attack risk coefficient Xgj is less than the risk threshold F, it indicates that the currently ongoing cloud video conference is not in an unsafe eavesdropping attack state, and no additional anti-attack test instruction is issued at this time.

7. A cloud video conferencing system based on quantum key encryption according to claim 6, characterized in that: The data construction module is used to construct an original loss information data set as a comparison benchmark after receiving an anti-attack test instruction and before introducing a verification key update frequency, based on monitoring the pre-introduction quantum signal transmission power Wr and the pre-introduction channel noise Wc in the spatial channel of the current cloud video conference participant; after introducing quantum key update frequencies of different intensities, monitor the post-introduction quantum signal transmission power Wrr and the post-introduction channel noise Wcc in the spatial channel of the cloud video conference participant to construct a verification loss information data set.

8. A cloud video conferencing system based on quantum key encryption according to claim 7, characterized in that: The anti-attack test module includes a post-attack risk analysis unit and an anti-attack test evaluation unit; The post-initiate attack risk analysis unit is used to obtain the post-initiate attack risk fluctuation coefficient Xyg after normalization according to the difference between the original loss information data set and the verification loss information data set. The post-initiate attack risk fluctuation coefficient Xyg is obtained by the following formula; ； Where Wr represents the quantum signal transmission power before introduction, Wc represents the channel noise before introduction, Wrr represents the quantum signal transmission power after introduction, and Wcc represents the channel noise after introduction. and are all expressed as weight values.

9. A cloud video conferencing system based on quantum key encryption according to claim 8, characterized in that: The anti-attack test evaluation unit is used to combine the acquired post-attack risk fluctuation coefficient Xyg with the attack risk coefficient Xgj, and after machine deep learning and dimensionless processing, fit to obtain the anti-attack test evaluation index Zcs, and the anti-attack test evaluation index Zcs is obtained by the following formula; ； In the formula, F represents the preset risk threshold, and Both are expressed as weight values, and A is expressed as a correction constant.

10. A cloud video conferencing system based on quantum key encryption according to claim 9, characterized in that: The defense balance module is used to pre-set the evaluation threshold G, and compare and analyze the anti-attack test evaluation index Zcs with the evaluation threshold G to comprehensively evaluate whether the currently introduced verification key update frequency is in a defense balance state. The specific process is as follows; If the anti-attack test evaluation index Zcs> the evaluation threshold G, it means that the currently introduced verification key update frequency is not in a defensive equilibrium state. At this time, the verification key update frequency will be enhanced, and the enhanced verification key update frequency will continue to be introduced until it is in a defensive equilibrium state to cope with the current eavesdropping attack behavior and meet the security and confidentiality requirements of cloud video conferencing; If the anti-attack test evaluation index Zcs = the evaluation threshold G, it means that the currently introduced verification key update frequency is in a defensive equilibrium state, meets the current cloud video conferencing security and confidentiality requirements, and maintains the current verification key update frequency, while continuing to implement the channel monitoring module to monitor the attack behavior; If the anti-attack test evaluation index Zcs is less than the evaluation threshold G, it means that the currently introduced verification key update frequency is not in a defensive equilibrium state. At this time, the verification key update frequency will be weakened, and the weakened verification key update frequency will continue to be introduced until it is in a defensive equilibrium state to deal with the current eavesdropping attack behavior and meet the security and confidentiality requirements of cloud video conferencing.

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