Security verification system for communication software

Abstract

The invention discloses a security verification system of communication software, relates to the technical field of security verification, and performs multi-mode biological characteristic dynamic combination verification, constructs a physical environment security situation awareness network, executes cognitive logic verification and behavior characteristic verification, establishes a quantum security communication channel, and detects and responds to non-voluntary operation behaviors. According to the method, dynamic physiological response, cross-layer feature fusion and environment-cognitive coupling verification are introduced, so that the safety is remarkably improved. Biological characteristics and environmental behaviors are dynamically monitored, and forgery attacks are effectively resisted. And a multi-layer safety protection and intelligent adjustment strategy ensures safety and optimizes the user experience at the same time. The edge computing and block chain technology protects privacy, and a three-level response protocol flexibly deals with different security threats.

Description

Technical Field

[0001] The present invention relates to the technical field of security verification, and in particular to a security verification system for communication software. Background Art

[0002] With the rapid development of information technology, communication software has become an indispensable part of people's daily life. However, the security issues of communication software are becoming increasingly prominent, such as identity fraud, information leakage, illegal monitoring, etc. These problems seriously threaten the privacy and data security of users. Traditional security verification methods, such as passwords and SMS verification codes, can no longer meet the current complex and changing security needs. Therefore, it is particularly urgent to develop a more comprehensive, efficient and intelligent security verification system.

[0003] At present, the Chinese patent with application number CN202110653403.6 discloses an automated verification method for the security of communication software, including the following steps: Step 1, obtain the corresponding software function call relationship diagram and export the corresponding HTML and XML files; Step 2, analyze and obtain the entry and exit functions of the communication software to be verified according to the exported HTML and XML files; Step 3, establish the execution list of the communication software; Step 4, establish a state machine model based on this; Step 5 write a security verification model based on the SPIN script; Step 6, analyze the security verification model through a model detection tool, generate a process model of the communication software, compare the design process of the communication software with the generated process model, and complete the consistency verification. The automated verification method for the security of the communication software does not require purely manual analysis and modeling, etc., so as to achieve the effect of automation, and improve the accuracy and consistency of the analysis of the communication software; improve efficiency and save labor costs.

[0004] It is difficult in the above technologies to dynamically adjust the verification problem strategy of the generative adversarial network according to environmental characteristics and biological characteristics, which affects the security of verification. Summary of the invention

[0005] The technical problem solved by the present invention is that it is difficult in the prior art to dynamically adjust the verification problem strategy of the generative adversarial network according to environmental characteristics and biological characteristics, which affects the security of the verification.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: A security verification system for communication software, including a biometric verification module, an environmental perception module, a behavior verification module, a quantum encryption module and a duress response module: The biometric verification module is used to perform dynamic combination verification of multi-modal biometric features; The environmental perception module is used to build a physical environment security situation awareness network; The behavior verification module is used to perform cognitive logic verification and behavior feature verification; The quantum encryption module is used to establish a quantum secure communication channel; The duress response module is used to detect and respond to involuntary operation behaviors.

[0007] Preferably, the biometric verification module includes a random selection unit, a liveness verification unit and an expression recognition unit: The random selection unit is used to obtain a communication initiation signal, randomly select two detection items for verification, the detection items include iris pulse synchronization detection, pressure sensing voiceprint detection and three-dimensional expression detection, and obtain a random verification result; The liveness verification unit is used to analyze the random verification result. If the random verification result is passed, a light source irradiation signal is output, a near-infrared spectrum sequence is obtained, and a subcutaneous capillary pulsation signal is obtained according to the near-infrared spectrum sequence. The waveform similarity is calculated according to the initial implanted reference file. If the waveform similarity meets the preset waveform similarity condition, the verification is passed. If the waveform similarity does not meet the preset waveform similarity condition, it is determined to be static imitation and the liveness verification result is output; The expression recognition unit is used to analyze the liveness verification result. If the liveness verification result is verified, the facial muscle movement trajectory is collected to identify abnormal micro-expression patterns.

[0008] Preferably, the environment perception module includes an electromagnetic fingerprint unit, a voiceprint modeling unit and a thermodynamic analysis unit; The electromagnetic fingerprint unit is used to scan the electromagnetic characteristics of the device within a radius of a first distance to obtain electromagnetic field fingerprint data; The voiceprint modeling unit compares the electromagnetic field fingerprint data with the preset environmental background voiceprint database to obtain the abnormal sound source frequency; The thermodynamic analysis unit is used to obtain the user's sitting posture thermal distribution map, divide the thermal image into N key areas, obtain the temperature gradient and hot spot morphology of each key area, record the thermal distribution changes according to a preset sampling frequency, and construct time series features. The time series features include the standard deviation of temperature fluctuations and the heat transfer rate. According to the initially implanted baseline behavior profile, it is detected whether it meets the baseline behavior profile.

[0009] Preferably, the behavior verification module includes a coupling verification unit, a behavior entropy analysis unit and an AI confrontation unit; The coupling verification unit is used to convert the electromagnetic field fingerprint into an encryption key fragment; The behavior entropy analysis unit is used to collect micro-behavior features, including input rhythm and cursor trajectory, and calculate the deviation from the preset benchmark file. The comparison threshold of the deviation is: ; The AI ​​adversarial unit is used to dynamically generate logic verification questions based on the deviation generative adversarial network and detect the consistency of the responses.

[0010] Preferably, the coupling verification unit performs the following operations: Cut the hash value of the electromagnetic field fingerprint data into hexadecimal key fragments to encrypt the memory puzzle content library; When an abnormal device is detected, insert false memory fragments according to the formula of the number of abnormal devices × 2; Generate a verification token that integrates the electromagnetic topology model and the memory sorting sequence. The calculation formula is: AuthenticationToken = SHA256(EMT Hash || MemoryScrapeTiming).

[0011] Preferably, the quantum encryption module includes a key distribution unit and a blockchain evidence storage unit; The key distribution unit is used to generate a session key through a quantum random number generator, and the session key is automatically destroyed after obtaining a communication end signal; The blockchain evidence storage unit uploads the electromagnetic field fingerprint data and the user's sitting posture thermal distribution map to the chain in real time to form a time-space stamp evidence chain.

[0012] Preferably, the duress response module includes a code trigger unit and a cognitive interference unit; The code trigger unit is used to activate the covert alarm according to a preset combination of biometric features; The cognitive interference unit is used to detect heart rate data. If the heart rate data is greater than a preset heart rate threshold, an error verification step is obtained from a preset error verification step library and implanted, and an alarm is triggered after the error verification step is detected.

[0013] Preferably, the security verification system for communication software also includes an adaptive strategy module, which is used to adjust the difficulty of the memory puzzle according to the stability of the electromagnetic field voiceprint data. If the electromagnetic field voiceprint data is a stable environment, the communication records before the first time period are verified. If the electromagnetic field voiceprint data is a fluctuating environment, the communication records within the second time period are verified. When a continuous electromagnetic anomaly is detected, the meta-memory problem is forced to be verified, and the time range for verifying the meta-memory problem is before the first time period or within the second time period.

[0014] Preferably, sensitive data is processed locally through edge computing, and blockchain is used to store hash values ​​of electromagnetic field voiceprint data.

[0015] Preferably, the three-level response protocol is: Level 1 response: When the electromagnetic deviation is greater than the first percentage and the memory errors are concentrated, the thermodynamic analysis unit is triggered; Secondary response: insert the radio frequency icon codeword fragment into the memory puzzle; Level 3 response: When the user selects the wrong arrangement of the codeword fragments, a silent alarm is activated and environmental data is uploaded to the supervisory end.

[0016] Beneficial effects of the invention: The invention significantly improves security by introducing dynamic physiological response, cross-layer feature fusion and environment-cognition coupling verification. Dynamic monitoring of biometrics and environmental behavior effectively resists counterfeit attacks. Multi-layer security protection and intelligent adjustment strategies ensure security while optimizing user experience. Edge computing and blockchain technology protect privacy, and the three-level response protocol flexibly responds to different security threats. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A basic flow chart of a security verification system for communication software provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0019] Example 1, reference Figure 1 , provides a security verification system for communication software, including a biometric verification module, an environmental perception module, a behavior verification module, a quantum encryption module and a coercion response module: The biometric verification module is used to perform dynamic combination verification of multi-modal biometric features.

[0020] The environmental perception module is used to build a physical environment security situation awareness network.

[0021] The behavior verification module is used to perform cognitive logic verification and behavior feature verification.

[0022] The quantum encryption module is used to establish a quantum secure communication channel.

[0023] The duress response module is used to detect and respond to involuntary operational behaviors.

[0024] The biometric verification module includes a random selection unit, a liveness verification unit, and an expression recognition unit: The random selection unit is used to obtain the communication initiation signal and randomly select two test items for verification, including iris pulse synchronization detection, pressure sensing voiceprint detection and three-dimensional expression detection, to obtain random verification results.

[0025] The random selection unit increases the unpredictability of the verification process by randomly selecting two test items for verification, making it difficult for counterfeiters to deceive specific test items. The test items cover multiple aspects such as iris pulse synchronization detection, pressure-sensitive voiceprint detection and three-dimensional expression detection, ensuring the comprehensiveness and accuracy of the verification.

[0026] The liveness verification unit is used to analyze the random verification result. If the random verification result is passed, the light source irradiation signal is output, the near-infrared spectrum sequence is obtained, the subcutaneous capillary pulsation signal is obtained according to the near-infrared spectrum sequence, and the waveform similarity is calculated according to the initial implanted reference file. If the waveform similarity meets the preset waveform similarity condition, the verification is passed. If the waveform similarity does not meet the preset waveform similarity condition, it is determined to be static imitation and the liveness verification result is output.

[0027] The liveness verification unit obtains the near-infrared spectral sequence and analyzes the subcutaneous capillary pulsation signal, and calculates the waveform similarity in combination with the initially implanted reference file, effectively distinguishing between real biological features and static imitations. The calculation of waveform similarity is based on the physiological characteristics of the subcutaneous capillary pulsation signal, and has high accuracy and stability, and can accurately determine the authenticity of the communication initiator.

[0028] The expression recognition unit is used to analyze the liveness verification result. If the liveness verification result is verified, the facial muscle movement trajectory is collected to identify abnormal micro-expression patterns.

[0029] When a user registers for the first time, the expression recognition unit records the following data to build a personalized micro-expression model: Natural expression baseline: the vibration frequency of each muscle group in a calm state.

[0030] Stress response threshold: micro-expressions induced by standardized stimuli are used to measure the muscle response latency.

[0031] Time (t), spatial coordinates (x, y, z), and muscle contraction intensity (I) are fused into a four-dimensional vector to generate a continuous space-time manifold of facial movement.

[0032] Calculate the acceleration mutation points of muscle groups, compare the symmetry of the left and right faces, extract the main frequency components of muscle vibration through FFT, detect unconventional frequency bands, input the four-dimensional motion map, and extract local spatiotemporal features through the 3D convolution layer.

[0033] Adopting DS evidence theory, the three-channel data of radar, vision and electromyography are weighted and fused to establish the abnormality judgment rules: Intensity threshold: The intensity of a single muscle contraction exceeds the baseline value by 200%.

[0034] Timing contradiction: Fear-related muscle activation detected during smile instructions.

[0035] Pattern conflict: The mouth is raised with the muscles around the eyes relaxed.

[0036] To make a graded response: Level 1 alarm: When the confidence level is >70%, the environmental perception module is triggered.

[0037] Level 2 blocking: Confidence level > 90% and lasts for 3 seconds, communication is terminated and the duress protocol is initiated.

[0038] Based on the liveness verification, the expression recognition unit further collects facial muscle movement trajectories and identifies abnormal micro-expression patterns, which helps to discover potential fraud or abnormal situations such as psychological pressure. Through the analysis of the expression recognition unit, the true intention and identity of the communication initiator can be further verified, thereby improving the security of the entire verification process.

[0039] The biometric verification module combines the random selection unit, liveness verification unit and expression recognition unit to achieve multi-dimensional and comprehensive verification of the identity of the communication initiator. This module not only improves the accuracy and security of verification, but also increases the difficulty of counterfeiting, effectively preventing static counterfeiting and fraud.

[0040] The environmental perception module includes an electromagnetic fingerprint unit, a voiceprint modeling unit and a thermodynamic analysis unit.

[0041] The electromagnetic fingerprint unit is used to scan the electromagnetic characteristics of the device within a first distance radius to obtain electromagnetic field fingerprint data.

[0042] The electromagnetic fingerprint unit can scan the electromagnetic characteristics of the device within a radius of the first distance, obtain detailed electromagnetic field fingerprint data, and by comparing the electromagnetic field fingerprint data, identify whether there are unauthorized or abnormal devices in the environment, thereby improving the security of the environment.

[0043] The voiceprint modeling unit compares the electromagnetic field fingerprint data with the preset environmental background soundprint database to obtain the abnormal sound source frequency.

[0044] The voiceprint modeling unit is based on the preset environmental background soundprint database. The unit can compare the electromagnetic field fingerprint data to identify the frequency of abnormal sound sources. By identifying the frequency of abnormal sound sources, the abnormal sound sources in the environment can be located, such as the voices of illegal intruders, the sounds of equipment failures, etc., providing important clues for security monitoring.

[0045] The thermodynamic analysis unit is used to obtain the user's sitting posture thermal distribution map, divide the thermal image into N key areas, obtain the temperature gradient and hot spot morphology of each key area, record the thermal distribution changes according to the preset sampling frequency, and construct time series features. The time series features include the standard deviation of temperature fluctuations and the heat transfer rate. According to the initially implanted baseline behavior profile, it is detected whether it meets the baseline behavior profile.

[0046] The thermodynamic analysis unit can obtain the thermal distribution map of the user's sitting posture. By dividing the key areas and analyzing the temperature gradient and hot spot morphology, it can understand the user's sitting habits. According to the preset sampling frequency, the changes in thermal distribution are recorded, and the time series characteristics are constructed. Compared with the initially implanted baseline behavior profile, it can detect whether the user behavior is consistent with the baseline behavior profile, such as maintaining a bad sitting posture for a long time, etc., providing data support for health monitoring and behavior analysis.

[0047] The environment perception module integrates the electromagnetic fingerprint unit, voiceprint modeling unit and thermodynamic analysis unit to achieve comprehensive, detailed and real-time perception and analysis of the surrounding environment. The module can effectively identify abnormal devices, abnormal sound sources and abnormal changes in user behavior in the environment, providing strong technical support for security monitoring, behavior analysis and other fields.

[0048] The behavior verification module includes a coupling verification unit, a behavior entropy analysis unit and an AI confrontation unit.

[0049] The coupling verification unit is used to convert the electromagnetic field fingerprint into an encryption key fragment.

[0050] The coupled verification unit performs the following operations: The hash value of the electromagnetic field fingerprint data is cut into hexadecimal key fragments to encrypt the memory puzzle content library.

[0051] When an abnormal device is detected, false memory fragments are inserted according to the formula of the number of abnormal devices × 2.

[0052] Generate a verification token that integrates the electromagnetic topology model and the memory sorting sequence. The calculation formula is: ; The coupled verification unit can convert electromagnetic field fingerprint data into encryption key fragments for encrypting the memory puzzle content library. This conversion increases the security of the data, making it difficult to crack even if the data is stolen. When an abnormal device is detected, the unit can insert false memory fragments at twice the number of abnormal devices. This approach increases the difficulty for attackers to identify real data, thereby improving the security of the system. By integrating the electromagnetic topology model with the memory sorting sequence, the coupled verification unit can generate a unique verification token. This token not only contains the physical feature information of the device, but also incorporates the user's behavioral habits, further enhancing the accuracy and reliability of the verification.

[0053] The behavior entropy analysis unit is used to collect micro-behavior features, including input rhythm and cursor trajectory, and calculate the deviation from the preset benchmark file. The comparison threshold of the deviation is: ; The behavior entropy analysis unit can collect micro-behavior features such as the user's input rhythm and cursor trajectory. These features are usually difficult to forge, so they help identify abnormal behavior. By comparing the collected micro-behavior features with the preset benchmark files, the deviation is calculated. This comparison can reveal whether the user's behavior is abnormal, thus providing a basis for subsequent security verification.

[0054] The AI ​​adversarial unit is used to dynamically generate logic verification questions based on the deviation generative adversarial network and detect the consistency of the responses.

[0055] The AI ​​adversarial unit can dynamically generate logic verification questions using a generative adversarial network based on the deviation calculated by the behavioral entropy analysis unit. These questions are both targeted and difficult to predict or bypass, thereby improving the effectiveness of verification. The unit can also detect the consistency of the user's answers to logic verification questions. By comparing the user's answers with the expected results, it can be further confirmed whether the user's identity and behavior are authentic.

[0056] The behavior verification module integrates multiple verification methods to achieve comprehensive, dynamic and intelligent verification of user behavior. This module can identify and respond to various potential security risks and improve the security and reliability of the system. Specifically, it ensures that the user's identity and behavior are authentic and trustworthy by analyzing the user's micro-behavioral characteristics, using electromagnetic field fingerprints for encryption verification, and generating adversarial logic verification questions.

[0057] The quantum encryption module includes a key distribution unit and a blockchain evidence storage unit.

[0058] The key distribution unit is used to generate a session key through a quantum random number generator, and the session key is automatically destroyed after obtaining a communication end signal.

[0059] The key distribution unit uses a quantum random number generator to generate session keys. Since quantum random numbers are truly random and unpredictable, the generated keys are extremely difficult to crack, thereby ensuring the security of the communication process. The session keys are automatically destroyed after the communication ends, avoiding the security risks that may be caused by long-term retention of the keys, and realizing dynamic and secure management of the keys. The key distribution process is fast and efficient, which can meet the needs of real-time communication, while ensuring timely updating and replacement of keys.

[0060] The blockchain evidence storage unit uploads the electromagnetic field fingerprint data and the user's sitting posture thermal distribution map to the chain in real time, forming a time-space stamp evidence chain.

[0061] Sensitive data is processed locally through edge computing, and blockchain is used to store the hash value of electromagnetic field voiceprint data.

[0062] The blockchain evidence storage unit uses blockchain technology to upload key information such as electromagnetic field fingerprint data and user sitting posture thermal distribution map to the chain in real time, forming a time-space stamp evidence chain. Due to the distributed ledger characteristics of the blockchain, once the data is recorded, it is difficult to tamper with or delete, ensuring the authenticity and integrity of the data. All data on the chain can be traced and verified, increasing the transparency and credibility of the data. When necessary, relevant data can be queried through the blockchain to prove the occurrence of an event or behavior. The data stored in the blockchain has legal evidentiary effect and can be used as the basis for legal proceedings or arbitration. This reduces the cost and time of dispute resolution to a certain extent.

[0063] The quantum encryption module achieves highly secure data encryption and evidence storage functions by combining advanced quantum technology and blockchain technology. It not only ensures the security of keys during communication and makes them difficult to crack, but also records and verifies relevant evidence information in real time through blockchain technology, thus enhancing the integrity and credibility of the data.

[0064] The duress response module includes a code trigger unit and a cognitive interference unit.

[0065] The code trigger unit is used to activate the covert alarm according to the preset biometric combination.

[0066] The code trigger unit relies on a preset biometric combination to activate the covert alarm. This method is highly concealed and not easily detected by potential threat actors, thus ensuring the secrecy and suddenness of the alarm action. Once the biometric combination is matched successfully, the code trigger unit can quickly activate the alarm system without the user having to perform additional operations, thus improving the response speed in emergency situations. Users can set a unique biometric combination according to their needs and habits, increasing the security and personalization of the system.

[0067] The cognitive interference unit is used to detect heart rate data. If the heart rate data is greater than a preset heart rate threshold, an error verification step is obtained from a preset error verification step library and implanted. An alarm is triggered after an error verification step is detected.

[0068] The cognitive interference unit can monitor the user's heart rate data in real time, and by comparing it with the preset heart rate threshold, it can accurately determine whether the user is in a possible state of duress or stress. When the user's heart rate rises abnormally, the cognitive interference unit will automatically select steps from the preset error verification step library and implant them into the user's verification process. These error steps are designed to confuse potential threat actors, making it difficult for them to determine the user's true intentions, while buying precious self-rescue time for users. After detecting an error verification step, the cognitive interference unit will immediately trigger an alarm, further enhancing the user's safety protection. This dual protection mechanism makes the duress response module more comprehensive and effective in responding to emergencies.

[0069] The duress response module can secretly trigger the alarm mechanism through a preset biometric combination when the user faces a duress situation, and automatically implant false verification steps to confuse potential threat actors when detecting possible psychological stress reactions of the user, while triggering an alarm to protect the user's safety. This module effectively improves the user's self-protection ability in emergency situations.

[0070] A security verification system for communication software also includes an adaptive strategy module, which is used to adjust the difficulty of memory puzzles according to the stability of electromagnetic field voiceprint data. If the electromagnetic field voiceprint data is a stable environment, the communication records before the first time period are verified. If the electromagnetic field voiceprint data is a fluctuating environment, the communication records within the second time period are verified. When continuous electromagnetic anomalies are detected, meta-memory problems are forced to be verified, and the time range for verifying the meta-memory problems is before the first time period or within the second time period.

[0071] The adaptive strategy module intelligently analyzes the stability of electromagnetic field voiceprint data, dynamically adjusts the difficulty of the memory puzzle, and specifically selects the time range for verifying communication records, thereby improving the flexibility and adaptability of the system while ensuring communication security.

[0072] The three-level response protocol is: Level 1 response: When the electromagnetic deviation is greater than the first percentage and the memory errors are concentrated, the thermodynamic analysis unit is triggered.

[0073] Secondary response: Insert the RF icon codeword fragment into the memory puzzle.

[0074] Level 3 response: When the user selects the wrong arrangement of the codeword fragments, a silent alarm is activated and environmental data is uploaded to the supervisory end.

[0075] This system randomly combines two of the three items of iris pulse, pressure voiceprint, and three-dimensional micro-expressions for each communication, and combines it with quantum dot live spectrum verification to break through the single-point vulnerability of traditional static biometrics, convert electromagnetic field fingerprints into memory puzzle encryption keys in real time, and dynamically insert interference items through the number of abnormal devices to achieve cross-anchoring of the physical environment and the user's long-term memory. The Mahalanobis distance deviation is calculated based on behavioral characteristics, and the verification problem strategy of the generative adversarial network is dynamically adjusted. Quantum random numbers generate single session keys, and key verification data is stored on the chain in real time to solve the vulnerability of traditional encryption relying on fixed certificates. Cognitive interference is triggered by heart rate thresholds, and a silent alarm is combined with a secret code biometric combination to protect the safety of the coerced person.

[0076] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A communication software security verification system, characterized in that: Including biological verification module, environmental perception module, behavior verification module, quantum encryption module and coercion response module: The biometric verification module is used to perform dynamic combination verification of multi-modal biometric features; The environmental perception module is used to build a physical environment security situation awareness network; The behavior verification module is used to perform cognitive logic verification and behavior feature verification; The quantum encryption module is used to establish a quantum secure communication channel; The duress response module is used to detect and respond to involuntary operation behaviors.

2. A communication software security verification system as claimed in claim 1, characterized in that: The biometric verification module includes a random selection unit, a liveness verification unit and an expression recognition unit: The random selection unit is used to obtain a communication initiation signal, randomly select two detection items for verification, the detection items include iris pulse synchronization detection, pressure sensing voiceprint detection and three-dimensional expression detection, and obtain a random verification result; The liveness verification unit is used to analyze the random verification result. If the random verification result is passed, a light source irradiation signal is output, a near-infrared spectrum sequence is obtained, and a subcutaneous capillary pulsation signal is obtained according to the near-infrared spectrum sequence. The waveform similarity is calculated according to the initial implanted reference file. If the waveform similarity meets the preset waveform similarity condition, the verification is passed. If the waveform similarity does not meet the preset waveform similarity condition, it is determined to be static imitation and the liveness verification result is output; The expression recognition unit is used to analyze the liveness verification result. If the liveness verification result is verified, the facial muscle movement trajectory is collected to identify abnormal micro-expression patterns.

3. A communication software security verification system as claimed in claim 2, characterized in that: The environment perception module includes an electromagnetic fingerprint unit, a voiceprint modeling unit and a thermodynamic analysis unit; The electromagnetic fingerprint unit is used to scan the electromagnetic characteristics of the device within a radius of a first distance to obtain electromagnetic field fingerprint data; The voiceprint modeling unit compares the electromagnetic field fingerprint data with the preset environmental background voiceprint database to obtain the abnormal sound source frequency; The thermodynamic analysis unit is used to obtain the user's sitting posture thermal distribution map, divide the thermal image into N key areas, obtain the temperature gradient and hot spot morphology of each key area, record the thermal distribution changes according to a preset sampling frequency, and construct time series features. The time series features include the standard deviation of temperature fluctuations and the heat transfer rate. According to the initially implanted baseline behavior profile, it is detected whether it meets the baseline behavior profile.

4. A communication software security verification system as claimed in claim 3, characterized in that: The behavior verification module includes a coupling verification unit, a behavior entropy analysis unit and an AI confrontation unit; The coupling verification unit is used to convert the electromagnetic field fingerprint into an encryption key fragment; The behavior entropy analysis unit is used to collect micro-behavior features, including input rhythm and cursor trajectory, and calculate the deviation from the preset benchmark file. The comparison threshold of the deviation is: ； The AI ​​adversarial unit is used to dynamically generate logic verification questions based on the deviation generative adversarial network and detect the consistency of the responses.

5. A communication software security verification system as claimed in claim 4, characterized in that: The coupling verification unit performs the following operations: Cut the hash value of the electromagnetic field fingerprint data into hexadecimal key fragments to encrypt the memory puzzle content library; When an abnormal device is detected, insert false memory fragments according to the formula of the number of abnormal devices × 2; Generate a verification token that integrates the electromagnetic topology model and the memory sorting sequence. The calculation formula is: AuthenticationToken = SHA256(EMT Hash || MemoryScrapeTiming).

6. A communication software security verification system as claimed in claim 5, characterized in that: The quantum encryption module includes a key distribution unit and a blockchain evidence storage unit; The key distribution unit is used to generate a session key through a quantum random number generator, and the session key is automatically destroyed after obtaining a communication end signal; The blockchain evidence storage unit uploads the electromagnetic field fingerprint data and the user's sitting posture thermal distribution map to the chain in real time to form a time-space stamp evidence chain.

7. A communication software security verification system as claimed in claim 6, characterized in that: The duress response module includes a code trigger unit and a cognitive interference unit; The code trigger unit is used to activate the covert alarm according to a preset combination of biometric features; The cognitive interference unit is used to detect heart rate data. If the heart rate data is greater than a preset heart rate threshold, an error verification step is obtained from a preset error verification step library and implanted, and an alarm is triggered after the error verification step is detected.

8. A communication software security verification system as claimed in claim 7, characterized in that: The security verification system for communication software also includes an adaptive strategy module, which is used to adjust the difficulty of the memory puzzle according to the stability of the electromagnetic field voiceprint data. If the electromagnetic field voiceprint data is a stable environment, the communication records before the first time period are verified. If the electromagnetic field voiceprint data is a fluctuating environment, the communication records within the second time period are verified. When a continuous electromagnetic anomaly is detected, the meta-memory problem is forced to be verified. The time range for verifying the meta-memory problem is before the first time period or within the second time period.

9. A communication software security verification system as claimed in claim 8, characterized in that: Sensitive data is processed locally through edge computing, and blockchain is used to store the hash value of electromagnetic field voiceprint data.

10. A communication software security verification system as claimed in claim 9, characterized in that: A communication software security verification system includes a three-level response protocol, wherein the three-level response protocol is: Level 1 response: When the electromagnetic deviation is greater than the first percentage and the memory errors are concentrated, the thermodynamic analysis unit is triggered; Secondary response: insert the radio frequency icon codeword fragment into the memory puzzle; Level 3 response: When the user selects the wrong arrangement of the codeword fragments, a silent alarm is activated and environmental data is uploaded to the supervisory end.

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