A security verification system for a communication software

Abstract

The present invention discloses a security verification system for a communication software, which relates to the technical field of security verification. It conducts multi-modal biometric dynamic combination verification, constructs a physical environment security situation awareness network, executes cognitive logic verification and behavior feature verification, establishes a quantum secure communication channel, detects and responds to involuntary operation behaviors. By introducing dynamic physiological response, cross-layer feature fusion and environment-cognition coupling verification, the present invention significantly improves security. Dynamically monitoring biometric features and environmental behaviors effectively resists forgery attacks. Multi-layer security protection and intelligent adjustment strategies ensure security while optimizing the user experience. Edge computing and blockchain technology protect privacy, and the three-level response protocol flexibly responds to different security threats.

Description

Technical Field

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

[0002] With the rapid development of information technology, communication software has become an indispensable part of people's daily lives. However, the security issues of communication software have become increasingly prominent, such as identity impersonation, information leakage, illegal monitoring, etc. These issues seriously threaten users' privacy and data security. Traditional security verification methods, such as passwords, SMS verification codes, etc., are difficult to meet the current complex and changeable security requirements. Therefore, it is particularly urgent to develop a more comprehensive, efficient, and intelligent security verification system.

[0003] Currently, the Chinese patent with the 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 graph and export the corresponding HTML and XML files; Step 2, analyze the exported HTML and XML files to obtain the entry and exit functions of the communication software to be verified; Step 3, establish an execution linked 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 checking tool to generate a process model of the communication software, and compare the design process of the communication software with the generated process model to complete the consistency verification. This automated verification method for the security of communication software does not require pure manual analysis and modeling, etc., thus achieving an automated effect, and improving the accuracy and consistency of the analysis of communication software; improving efficiency and saving labor costs.

[0004] In the above technology, it is difficult to dynamically adjust the verification problem strategy of the generative adversarial network according to environmental characteristics and biometric characteristics, which affects the security of verification. Summary of the Invention

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

[0006] To solve the above technical problem, the present invention provides the following technical solutions:

[0007] A security verification system for a communication software, including a biometric verification module, an environmental perception module, a behavior verification module, a quantum encryption module, and a coercion response module:

[0008] The biometric verification module is used for performing multi-modal biometric dynamic combination verification;

[0009] The environmental perception module is used to construct a physical environment security situation perception network;

[0010] The behavior verification module is used to perform cognitive logic verification and behavior feature verification;

[0011] The quantum encryption module is used to establish a quantum secure communication channel;

[0012] The coercion response module is used to detect and respond to involuntary operation behaviors;

[0013] The biometric verification module includes a random selection unit, a liveness verification unit, and an expression recognition unit:

[0014] 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-sensitive voiceprint detection, and three-dimensional expression detection, and obtain a random verification result;

[0015] The liveness verification unit is used to analyze the random verification result. If the random verification result is a pass, it outputs a light source irradiation signal, obtains a near-infrared spectral sequence, obtains a subcutaneous capillary pulsation signal according to the near-infrared spectral sequence, calculates the waveform similarity according to the initially implanted reference file. If the waveform similarity meets the preset waveform similarity condition, it passes the verification. If the waveform similarity does not meet the preset waveform similarity condition, it is determined as static forgery and outputs a liveness verification result;

[0016] The expression recognition unit is used to analyze the liveness verification result. If the liveness verification result is a pass, it collects the facial muscle movement trajectory and recognizes abnormal micro-expression patterns;

[0017] The environmental perception module includes an electromagnetic fingerprint unit, a voiceprint modeling unit, and a thermodynamics analysis unit;

[0018] The electromagnetic fingerprint unit is used to scan the electromagnetic characteristics of devices within a radius of the first distance and obtain electromagnetic field fingerprint data;

[0019] The voiceprint modeling unit compares the electromagnetic field fingerprint data according to a preset environmental background sound ripple database and obtains an abnormal sound source frequency;

[0020] The thermodynamics analysis unit is used to obtain a user sitting posture heat distribution map, divide the heat image into N key regions, obtain the temperature gradient and hot spot morphology of each key region, record the heat distribution change according to a preset sampling frequency, construct time series features. The time series features include the standard deviation of temperature fluctuations and the heat transfer rate, and detect whether it conforms to the initially implanted reference behavior file according to the initially implanted reference behavior file;

[0021] The behavior verification module includes a coupling verification unit, a behavior entropy analysis unit, and an AI confrontation unit;

[0022] The coupling verification unit is used to convert the electromagnetic field fingerprint into an encrypted key fragment;

[0023] The behavior entropy analysis unit is used to collect microscopic behavior characteristics, the microscopic behavior characteristics include input rhythm and cursor trajectory, calculate the deviation from a preset reference profile, and the comparison threshold of the deviation is:

[0024] ;

[0025] The AI confrontation unit is used to dynamically generate logical verification questions through a generative adversarial network according to the deviation and detect the response consistency.

[0026] Preferably, the coupling verification unit performs the following operations:

[0027] Cut the hash value of the electromagnetic field fingerprint data into a hexadecimal key fragment and encrypt the memory puzzle content library;

[0028] When an abnormal device is detected, insert false memory fragments according to the formula of the number of abnormal devices × 2;

[0029] Generate a verification token that fuses the electromagnetic topology model and the memory sorting time sequence, and the calculation formula is:

[0030] Verification token = SHA256(electromagnetic topology model hash || memory fragment sorting time sequence).

[0031] Preferably, the quantum encryption module includes a key distribution unit and a blockchain evidence storage unit;

[0032] 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 the communication end signal;

[0033] The blockchain evidence storage unit uploads the electromagnetic field fingerprint data and the user sitting posture heat distribution map to the chain in real time to form a space-time stamp evidence chain.

[0034] Preferably, the coercion response module includes a cipher trigger unit and a cognitive interference unit;

[0035] The cipher trigger unit is used to activate the covert alarm according to a preset biometric combination;

[0036] The cognitive interference unit is used to detect the heart rate data. If the heart rate data is greater than a preset heart rate threshold, obtain the wrong verification steps from a preset wrong verification step library and implant them, and trigger an alarm after detecting the wrong verification steps.

[0037] Preferably, the security verification system of a communication software further includes an adaptive policy module, which is used to adjust the difficulty of the memory puzzle according to the stability of the electromagnetic field fingerprint data. If the electromagnetic field fingerprint data is in a stable environment, the communication records before the first time period are verified. If the electromagnetic field fingerprint data is in a fluctuating environment, the communication records within the second time period are verified. When continuous electromagnetic anomalies are detected, the meta-memory problem is forcibly verified, and the time range for verifying the meta-memory problem is before the first time period or within the second time period.

[0038] Preferably, sensitive data is locally processed through edge computing, and the blockchain is used to store the hash value of the electromagnetic field fingerprint data.

[0039] Preferably, the three-level response protocol is as follows:

[0040] First-level response: When the electromagnetic deviation degree is greater than the first percentage and memory errors are concentrated, the thermodynamics analysis unit is triggered;

[0041] Second-level response: Insert RF icon cipher fragments into the memory puzzle;

[0042] Third-level response: When the user selects the wrong arrangement of the cipher fragments, a silent alarm is activated and the environmental data is uploaded to the supervision end.

[0043] Advantages of the present invention: By introducing dynamic physiological responses, cross-layer feature fusion, and environment-cognition coupling verification, the present invention significantly improves security. Dynamically monitoring biometric features and environmental behaviors effectively resists forgery attacks. Multi-layer security protection and intelligent adjustment strategies ensure security while optimizing the user experience. Edge computing and blockchain technologies protect privacy, and the three-level response protocol flexibly responds to different security threats. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the basic process of a security verification system of a communication software provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention is made in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments.

[0046] Embodiment 1, referring to Figure 1 , a security verification system of a communication software is provided, including a biometric verification module, an environmental perception module, a behavior verification module, a quantum encryption module, and a coercion response module:

[0047] The biometric verification module is used to perform multi-modal biometric dynamic combination verification.

[0048] The environmental perception module is used to construct a physical environment security situation awareness network.

[0049] The behavior verification module is used to perform cognitive logic verification and behavior characteristic verification.

[0050] The quantum encryption module is used to establish a quantum secure communication channel.

[0051] The coercion response module is used to detect and respond to involuntary operation behaviors.

[0052] The biometric verification module includes a random selection unit, a liveness verification unit, and an expression recognition unit:

[0053] 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.

[0054] By randomly selecting two detection items for verification, the random selection unit increases the unpredictability of the verification process, making it difficult for counterfeiters to deceive against specific detection items. The detection items cover multiple aspects such as iris pulse synchronization detection, pressure-sensing voiceprint detection, and three-dimensional expression detection, ensuring the comprehensiveness and accuracy of the verification.

[0055] The liveness verification unit is used to analyze the random verification result. If the random verification result is a pass, it outputs a light source irradiation signal, obtains a near-infrared spectrum sequence, obtains a subcutaneous capillary pulsation signal according to the near-infrared spectrum sequence, calculates the waveform similarity according to the initially implanted reference file. If the waveform similarity meets the preset waveform similarity condition, it passes the verification. If the waveform similarity does not meet the preset waveform similarity condition, it is determined as a static forgery and outputs the liveness verification result.

[0056] By obtaining the near-infrared spectrum sequence and analyzing the subcutaneous capillary pulsation signal, and calculating the waveform similarity in combination with the initially implanted reference file, the liveness verification unit effectively distinguishes real biometric features from static forgeries. The calculation of the waveform similarity is based on the physiological feature of the subcutaneous capillary pulsation signal, with high accuracy and stability, and can accurately judge the authenticity of the communication initiator.

[0057] The expression recognition unit is used to analyze the liveness verification result. If the liveness verification result is a pass, it collects the facial muscle movement trajectory and recognizes abnormal micro-expression patterns.

[0058] When the user first registers, the expression recognition unit records the following data to construct a personalized micro-expression model:

[0059] Natural expression baseline: The vibration frequency of each muscle group in a calm state.

[0060] Stress response threshold: Induce microexpressions through standardized stimuli and measure the muscle response delay.

[0061] Fuse time (t), spatial coordinates (x, y, z), and muscle contraction intensity (I) into a four-dimensional vector to generate a continuous spatio-temporal manifold of facial movements.

[0062] Calculate the mutation points of muscle group acceleration, compare the left and right facial symmetry, extract the main frequency components of muscle vibration through FFT, detect unconventional frequency bands, input into the four-dimensional motion atlas, and extract local spatio-temporal features through 3D convolutional layers.

[0063] Adopt the D-S evidence theory to weight and fuse the data of radar, vision, and electromyogram three channels, and establish an abnormal determination rule:

[0064] Intensity threshold: The contraction intensity of a single muscle exceeds 200% of the reference value.

[0065] Temporal contradiction: Fear-related muscle activation is detected under a smile command.

[0066] Pattern conflict: The mouth is upturned accompanied by relaxation of the periorbital muscles.

[0067] Perform hierarchical response:

[0068] Level 1 alarm: When the confidence level > 70%, trigger the environmental perception module.

[0069] Level 2 block: When the confidence level > 90% and lasts for 3 seconds, terminate the communication and initiate the coercion protocol.

[0070] Based on the passing of live verification, the expression recognition unit further collects the facial muscle movement trajectories and recognizes abnormal microexpression patterns, which helps to detect potential abnormal situations such as fraud or psychological stress. Through the analysis of the expression recognition unit, the true intention and identity of the communication initiator can be further verified, improving the security of the entire verification process.

[0071] The biometric verification module combines three units: a random selection unit, a live verification unit, and an expression recognition unit, realizing 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 forgery and fraud.

[0072] The environmental perception module includes an electromagnetic fingerprint unit, a voiceprint modeling unit, and a thermodynamics analysis unit.

[0073] The electromagnetic fingerprint unit is used to scan the electromagnetic characteristics of devices within a radius of the first distance to obtain electromagnetic field fingerprint data.

[0074] The electromagnetic fingerprint unit can scan the electromagnetic characteristics of devices within a radius of the first distance, obtain detailed electromagnetic field fingerprint data, and by comparing the electromagnetic field fingerprint data, it can identify whether there are unauthorized or abnormal devices in the environment, improving the security of the environment.

[0075] The voiceprint modeling unit compares the electromagnetic field fingerprint data according to a preset environmental background sound ripple database to obtain the abnormal sound source frequency.

[0076] The voiceprint modeling unit can compare the electromagnetic field fingerprint data according to a preset environmental background sound ripple database to identify the abnormal sound source frequency. By identifying the abnormal sound source frequency, it can locate the abnormal sound source in the environment, such as the sound of an illegal intruder or equipment failure, providing important clues for security monitoring.

[0077] The thermodynamic analysis unit is used to obtain the user's sitting posture heat distribution map, divide the heat image into N key regions, obtain the temperature gradient and hot spot morphology of each key region, record the change of heat distribution according to the preset sampling frequency, construct time series features, and the time series features include the standard deviation of temperature fluctuation and heat transfer rate. According to the initially implanted baseline behavior profile, it detects whether it conforms to the baseline behavior profile.

[0078] The thermodynamic analysis unit can obtain the heat distribution map of the user's sitting posture. By dividing the key regions and analyzing the temperature gradient and hot spot morphology, it can understand the user's sitting posture habits, record the change of heat distribution according to the preset sampling frequency, construct time series features, and compare them with the initially implanted baseline behavior profile to detect whether the user's behavior conforms to the baseline behavior profile, such as maintaining a bad sitting posture for a long time, providing data support for health monitoring and behavior analysis.

[0079] The environmental perception module integrates the electromagnetic fingerprint unit, the voiceprint modeling unit, and the thermodynamic analysis unit to achieve comprehensive, detailed, and real-time perception and analysis of the surrounding environment. This module can effectively identify abnormal devices, abnormal sound sources, and abnormal changes in user behavior in the environment, providing strong technical support for fields such as security monitoring and behavior analysis.

[0080] The behavior verification module includes a coupling verification unit, a behavior entropy analysis unit, and an AI confrontation unit.

[0081] The coupling verification unit is used to convert the electromagnetic field fingerprint into an encrypted key fragment.

[0082] The coupling verification unit performs the following operations:

[0083] Cut the hash value of the electromagnetic field fingerprint data into hexadecimal key fragments and encrypt the memory puzzle content library.

[0084] When an abnormal device is detected, false memory fragments are inserted according to the formula of the number of abnormal devices × 2.

[0085] Generate a verification token by fusing the electromagnetic topology model and the memory sorting time sequence. The calculation formula is:

[0086] ;

[0087] The coupling verification unit can convert the electromagnetic field fingerprint data into encrypted key fragments for encrypting the memory puzzle content library. This conversion enhances data security, making it difficult to crack even if the data is stolen. When an abnormal device is detected, the unit can insert false memory fragments twice the number of abnormal devices. This approach increases the difficulty for attackers to identify real data, thereby improving the system's security. By fusing the electromagnetic topology model and the memory sorting time sequence, the coupling verification unit can generate unique verification tokens. These tokens not only contain the physical characteristic information of the device but also incorporate the user's behavior habits, further enhancing the accuracy and reliability of verification.

[0088] The behavioral entropy analysis unit is used to collect micro-behavioral characteristics, which include input rhythm and cursor trajectory, and calculate the deviation from a preset reference profile. The comparison threshold for the deviation is:

[0089] ;

[0090] The behavioral entropy analysis unit can collect micro-behavioral characteristics such as the user's input rhythm and cursor trajectory, which are usually difficult to forge, thus helping to identify abnormal behaviors. By comparing the collected micro-behavioral characteristics with a preset reference profile, the deviation is calculated. This comparison can reveal whether the user's behavior is abnormal, providing a basis for subsequent security verification.

[0091] The AI adversarial unit is used to dynamically generate logical verification questions based on the deviation using a generative adversarial network and detect the response consistency.

[0092] Based on the deviation calculated by the behavioral entropy analysis unit, the AI adversarial unit can dynamically generate logical verification questions using a generative adversarial network. These questions are both targeted and difficult to predict or bypass, thus improving the effectiveness of verification. The unit can also detect the response consistency of the user to the logical verification questions. By comparing the user's response with the expected result, the authenticity and credibility of the user's identity and behavior can be further confirmed.

[0093] The behavior verification module realizes comprehensive, dynamic and intelligent verification of user behavior by integrating various verification means. This module can identify and respond to various potential security risks, improving the security and reliability of the system. Specifically, it ensures that the user's identity and behavior are real and trustworthy by analyzing the user's micro-behavioral characteristics, using electromagnetic field fingerprints for encryption verification, and generating adversarial logical verification questions.

[0094] The quantum encryption module includes a key distribution unit and a blockchain evidence storage unit.

[0095] 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 the communication end signal.

[0096] The key distribution unit uses a quantum random number generator to generate a session key. Since quantum random numbers have true randomness and unpredictability, the generated key is extremely difficult to crack, thus ensuring the security of the communication process. The session key is automatically destroyed after the communication ends, avoiding the security risks that may be brought by the long-term retention of the key, realizing the dynamic and secure management of the key. The key distribution process is fast and efficient, can meet the needs of real-time communication, and at the same time ensures the timely update and replacement of the key.

[0097] The blockchain evidence storage unit uploads the electromagnetic field fingerprint data and the user sitting posture thermal distribution map to the blockchain in real time to form a time-space stamp evidence chain.

[0098] Sensitive data is locally processed through edge computing, and the blockchain is used to store the hash value of the electromagnetic field fingerprint data.

[0099] The blockchain evidence storage unit uploads key information such as electromagnetic field fingerprint data and the user sitting posture thermal distribution map to the blockchain in real time through blockchain technology to form a time-space stamp evidence chain. Due to the distributed ledger feature of the blockchain, once the data is recorded, it is difficult to be tampered with or deleted, ensuring the authenticity and integrity of the data. All data uploaded to the blockchain can be traced and verified, increasing the transparency and credibility of the data. When needed, relevant data can be queried through the blockchain to prove the occurrence of a certain event or behavior. The data stored in the blockchain for evidence has legal evidentiary effect and can be used as the basis for legal litigation or arbitration. This reduces the cost and time of dispute resolution to a certain extent.

[0100] The quantum encryption module realizes highly secure data encryption and evidence storage functions by combining advanced quantum technology and blockchain technology. It can not only ensure the security of the key during the communication process and make it difficult to be cracked, but also record and verify relevant evidence information in real time through blockchain technology, enhancing the integrity and credibility of the data.

[0101] The coercion response module includes a password trigger unit and a cognitive interference unit.

[0102] The password trigger unit is used to activate the covert alarm according to a preset combination of biometric features.

[0103] The password trigger unit relies on a preset combination of biometric features to activate the covert alarm. This method has strong concealment and is not easily detected by potential threats, thus ensuring the secrecy and suddenness of the alarm action. Once the biometric feature combination is successfully matched, the password trigger unit can quickly activate the alarm system without additional operations by the user, improving the response speed in case of emergency. Users can set unique biometric feature combinations according to their own needs and habits, increasing the security and personalization of the system.

[0104] 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.

[0105] The cognitive interference unit can monitor the user's heart rate data in real time. By comparing it with the preset heart rate threshold, it can accurately judge whether the user is in a possible coercion or stress state. When the user's heart rate abnormally increases, 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 threats and make it difficult for them to judge the user's true intention, while buying precious self-help time for the user. After the error verification step is detected, the cognitive interference unit will immediately trigger an alarm, further enhancing the user's security protection. This dual protection mechanism makes the coercion response module more comprehensive and effective in dealing with emergencies.

[0106] The coercion response module can secretly trigger the alarm mechanism through a preset combination of biometric features when the user faces a coercion situation, and automatically implant error verification steps to confuse potential threats when detecting the user's possible psychological stress response, while triggering an alarm to protect the user's safety. This module effectively improves the user's self-protection ability in an emergency.

[0107] A security verification system for a communication software further includes an adaptive policy module. The adaptive policy module is used to adjust the difficulty of the memory puzzle according to the stability of the electromagnetic field fingerprint data. If the electromagnetic field fingerprint data is in a stable environment, the communication records before the first time period are verified. If the electromagnetic field fingerprint data is in a fluctuating environment, the communication records within the second time period are verified. When continuous electromagnetic anomalies are detected, the meta-memory problem is forcibly verified, and the time range for verifying the meta-memory problem is before the first time period or within the second time period.

[0108] The adaptive strategy module dynamically adjusts the difficulty of the memory puzzle and selectively chooses the time range for verifying communication records by intelligently analyzing the stability of electromagnetic field fingerprint data, thereby improving the flexibility and adaptability of the system while ensuring communication security.

[0109] The three-level response protocol is as follows:

[0110] First-level response: When the electromagnetic deviation degree is greater than the first percentage and memory errors are concentrated, trigger the thermodynamic analysis unit.

[0111] Second-level response: Insert RF icon cipher fragments into the memory puzzle.

[0112] Third-level response: When the user selects an incorrect arrangement of cipher fragments, activate the silent alarm and upload environmental data to the supervision end.

[0113] In each communication, this system randomly combines two of the three items of iris pulse, pressure voiceprint, and three-dimensional micro-expression, combines with quantum dot in-vivo spectrum verification, breaks through the single-point vulnerability of traditional static biometric features, converts electromagnetic field fingerprints into memory puzzle encryption keys in real time, and realizes cross-anchoring of the physical environment and the user's long-term memory by dynamically inserting interference items based on the number of abnormal devices. Calculate the Mahalanobis distance deviation degree based on behavioral characteristics, dynamically adjust the verification problem strategy of the generative adversarial network, generate a single-session key with quantum random numbers, and store key verification data on the chain in real time to solve the vulnerability of traditional encryption relying on fixed certificates. Trigger cognitive interference through the heart rate threshold, combine with silent alarm using password biometric features to protect the safety of the coerced.

[0114] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media that contain computer-usable program code. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the functions specified in one process Figure 1 one process or multiple processes and / or boxes Figure 1 specified in one box or multiple boxes.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by 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; 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; 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 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 file, detect whether it meets the baseline behavior file; 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.

2. A communication software security verification system as claimed in claim 1, 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).

3. A communication software security verification system as claimed in claim 2, 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.

4. A communication software security verification system as claimed in claim 3, 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.

5. A communication software security verification system as claimed in claim 4, 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 fingerprint data. If the electromagnetic field fingerprint data is a stable environment, the communication records before the first time period are verified. If the electromagnetic field fingerprint 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.

6. A communication software security verification system as claimed in claim 5, characterized in that: Sensitive data is processed locally through edge computing, and blockchain is used to store hash values ​​of electromagnetic field fingerprint data.

7. A communication software security verification system as claimed in claim 6, 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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