A program behavior intervention method based on quantum states

Abstract

The present invention provides a method for program behavior intervention based on quantum states. By quantizing the target program state and generating quantum states, the synchronous monitoring of multiple program behaviors is achieved by utilizing the quantum entanglement effect. The malicious behavior tendency of the program is detected by defining a quantum threshold function, and a quantum control operator is used to intervene in the program quantum state to adjust the program state and prevent the occurrence of malicious behaviors. In addition, a random perturbation term is introduced to increase the system complexity, and the intervention strategy is dynamically adjusted through multiple quantum state measurements and feedback to optimize the control parameters. The present invention effectively solves the problems of insufficient detection accuracy and weak defense ability faced by traditional program behavior detection technologies when dealing with complex program behaviors, significantly improves the security and stability of the system, and enhances the detection and defense capabilities against complex malicious behaviors.

Description

Technical Field

[0001] The present invention relates to the field of network security, and specifically to a method for program behavior intervention based on quantum states. Background Art

[0002] At present, with the rapid development of information technology, various computer programs are widely used in key fields such as industrial control, financial transactions, and data processing. However, with the increase in program complexity, the threat of malicious behavior is also escalating continuously, bringing serious hidden dangers to network security. Traditional program behavior detection and protection methods often show certain limitations when facing complex malicious behaviors and are difficult to ensure the overall security of the system.

[0003] Existing program behavior detection technologies mainly rely on predefined rules and feature libraries for matching and analysis. However, these methods have a certain lag when facing new, variant, or unknown malicious behaviors and cannot provide timely and effective early warnings and protection. At the same time, most traditional protection measures are for post-detection and response and lack the ability of proactive pre-intervention, resulting in irreversible damage to the system once malicious behavior occurs.

[0004] In addition, existing security protection mechanisms are difficult to effectively coordinate and monitor the associated behaviors between different systems when dealing with distributed systems and multi-program environments. This limitation allows malicious behavior to spread through coordinated attacks between systems, further increasing the difficulty of protection and unable to meet the security requirements in modern complex network environments. Summary of the Invention

[0005] To solve the problems of insufficient accuracy in malicious behavior detection, monitoring challenges in dynamic environments, lag in real-time intervention, and system predictability, the present invention proposes a method for program behavior intervention based on quantum states. This method quantizes the program state, uses quantum entanglement for synchronous monitoring, uses a quantum threshold function to improve the accuracy of malicious behavior detection, and combines quantum control operators for real-time intervention. In addition, by introducing random perturbations to increase system complexity, multiple quantum state measurements and dynamic feedback adjustment of intervention strategies are carried out to achieve efficient adaptive adjustment and enhanced anti-attack capabilities.

[0006] A method for program behavior intervention based on quantum states, comprising the following steps:

[0007] (1) Quantum state initialization: Quantize the initial state of the target program to construct the quantum state |Ψ(t)> of the program, which is represented by the following mathematical expression:

[0008] |Ψ(t)> = α(t)|0> + β(t)|1>

[0009] Among them, α(t) and β(t) are functions of time t, representing the probability amplitude of the program in different quantum bit states, satisfying the normalization condition |α(t)| 2 +|β(t)| 2 =1;

[0010] (2) Quantum entanglement effect monitoring: By introducing the quantum entanglement effect, the quantum states of multiple systems are entangled, and the synchronous monitoring of multiple program behaviors is achieved. The mathematical expression of the entangled state is:

[0011]

[0012] This expression is used to describe quantum correlations between systems to ensure the simultaneous monitoring of the behavior of multiple programs;

[0013] (3) Malicious behavior detection: A quantum threshold function F(Ψ(t)) is defined to detect possible malicious behavior of the program. The quantum threshold function is based on the following formula:

[0014]

[0015] Among them, γ i is the weight coefficient, is a Hamiltonian operator used to calculate the expected value of the program quantum state to assess the risk of malicious behavior;

[0016] (4) Quantum state intervention: After detecting malicious behavior tendencies, the quantum control operator U(θ(t)) is used to intervene in the quantum state of the target program and change its quantum state evolution path. The mathematical expression is:

[0017]

[0018] Among them, θ(t) is the control angle, which is used to adjust the quantum state of the program to prevent malicious behavior;

[0019] (5) Introduction of random disturbance: A random disturbance term R(∈(t)) is introduced during the intervention process. The expression of this random disturbance term is:

[0020] R(∈(t))=∈(t)·|η(t)>

[0021] Among them, ∈(t) is the random perturbation coefficient, η(t) is the random state parameter, and the perturbation increases the complexity of the system, making it difficult for attackers to predict and exploit;

[0022] (6) Multiple quantum state measurements and feedback: Multiple measurements are performed on the adjusted quantum state, and the measurement operator Defined as:

[0023]

[0024] Through the feedback of multiple measurement results, the intervention strategy of the system is dynamically adjusted to ensure the safety and stability of program behavior.

[0025] (7) System self-adaptive regulation: According to the measurement feedback of the quantum state, dynamically adjust the control angle θ(t), weight coefficient γ i and the random perturbation coefficient ∈(t), and then substitute the weight coefficient γ i into the malicious behavior detection in step (3), substitute the control angle θ(t) into the quantum state intervention in step (4), and substitute the random perturbation coefficient ∈(t) into the random perturbation introduction in step (5) to optimize the intervention effect.

[0026] Furthermore, in the quantum state initialization in step (1), the probability amplitude coefficients α(t) and β(t) are determined by the following nonlinear differential equations:

[0027]

[0028] where and are the partial derivatives of the system Hamiltonian H with respect to the complex conjugates of α(t) and β(t).

[0029] Furthermore, in the malicious behavior detection in step (3), the quantum threshold function F(Ψ(t)) used optimizes the weight coefficient γ i through a machine learning algorithm to adapt to the behavior patterns of different types of programs.

[0030] Furthermore, in the quantum state intervention in step (4), the control angle θ(t) of the control operator U(θ(t)) is adjusted in real time through a feedback mechanism to adapt to different malicious behavior patterns.

[0031] Furthermore, in the multiple quantum state measurements and feedback in step (6), the measurement operator is dynamically adjusted based on real-time measurement data to ensure the stability of the system under uncertain conditions.

[0032] The present invention has the following advantages:

[0033] 1. High-precision malicious behavior detection: Through quantum state initialization and the quantum threshold function, the accuracy of malicious behavior detection is significantly improved, the false alarm rate and missed alarm rate are effectively reduced, and the accurate identification of complex attack patterns is ensured.

[0034] 2. Real-time intervention ability: Applying the quantum control operator to achieve instant adjustment of the program quantum state, quickly responding to potential threats, significantly reducing the response delay in traditional protection measures, and improving the real-time protection ability of the system.

[0035] 3. Dynamic monitoring adaptability: The quantum entanglement effect makes it possible to synchronously monitor the behavior of multiple programs, which can adapt to the rapidly changing network environment in real time and improve the monitoring effect of dynamic program behavior.

[0036] 4. Enhanced anti-attack capabilities: The introduction of quantum random perturbations increases the complexity of the system, making it difficult for attackers to predict system behavior, enhancing defense capabilities against new attacks, and improving the overall security of the system.

[0037] 5. Intelligent adaptive regulation: A dynamic adjustment mechanism based on quantum state measurement feedback enables real-time optimization of control parameters and intervention strategies, ensuring the stability and efficiency of the system in complex and dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flowchart of a method for intervening in program behavior based on quantum states according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 making creative efforts shall fall within the scope of protection of the present invention.

[0040] Figure 1 A method for intervening in program behavior based on quantum state according to an embodiment of the present invention is shown, comprising the following steps:

[0041] (1) Quantum state initialization: Quantize the initial state of the target program to construct the program's quantum state |Ψ(t)>, which is represented by the following mathematical expression:

[0042] |Ψ(t)>=α(t)|0>+β(t)|1>

[0043] Among them, α(t) and β(t) are functions of time t, representing the probability amplitude of the program in different quantum bit states, satisfying the normalization condition |α(t)|2+|β(t)| 2 = 1. |0> and |1> are the two basic states of a single quantum bit, representing the binary states 0 and 1 in classical computing.

[0044] (2) Quantum entanglement effect monitoring: By introducing the quantum entanglement effect, the quantum states of multiple systems are entangled to achieve synchronous monitoring of multiple program behaviors. The entangled state is obtained through quantum state operations, and the mathematical expression of the entangled state is:

[0045]

[0046] |00> represents the composite state of two qubits, where the first qubit is in the |0> state and the second qubit is also in the |0> state. |11> represents the state where both qubits are in the |1> state. The expression |00> + |11> represents the superposition state of two qubits. Since the probability amplitudes of quantum states need to satisfy the normalization condition, that is, the sum of the probabilities of all possible states must equal 1, we introduce a normalization factor This expression is used to describe the quantum correlation between systems to ensure the monitoring of the behaviors of multiple programs simultaneously. The above formula is for obtaining an entangled state through quantum state operations.

[0047] (3) Malicious behavior detection: Define a quantum threshold function F(Ψ(t)) to detect possible malicious behaviors of the program. The formula of the quantum threshold function is as follows:

[0048]

[0049] where γ i is the weight coefficient, is the Hamiltonian operator, which is used to calculate the expected value of the program's quantum state to evaluate the risk of malicious behavior. The quantum state threshold during the normal operation of the system is λ. Normal behavior: If F(Ψ(t)) ≤ λ, the program behavior is considered normal and no further action is required. Malicious behavior: If F(Ψ(t)) > λ, it is detected that the program may have malicious behavior and corresponding defense mechanisms need to be triggered or interventions made.

[0050] (4) Quantum state intervention: After detecting the tendency of malicious behavior, U(θ(t)) will transform the current quantum state and change its future evolution path. For example, assume that the system is evolving from a safe state |Ψ safe > to an unsafe |Ψ threat >. At this time, by applying U(θ(t)), the quantum state can be shifted from the original evolution path and returned to a safer state, changing its quantum state evolution path. The mathematical expression is:

[0051]

[0052] where θ(t) is the control angle, and the operator is used to adjust the quantum state of the program to prevent the occurrence of malicious behavior.

[0053] (5) Introduction of random perturbation: Introduce a random perturbation term R(∈(t)) during the intervention process. The expression of this perturbation term is:

[0054] R(∈(t)) = ∈(t)·|η(t)>

[0055] Among them, ∈(t) is the random perturbation coefficient, η(t) is the random state parameter, and the perturbation increases the complexity of the system, making it difficult for attackers to predict and exploit. For example:

[0056] For example, if the perturbation term we introduce is: And ∈(t)=0.1, then the disturbance term is: After the intervention, the quantum state becomes: |Ψ′(t)>=|Ψ(t)>+R(∈(t)) In the above example:

[0057] (6) Multiple quantum state measurements and feedback: Multiple measurements are performed on the adjusted quantum state, and the measurement operator Defined as:

[0058]

[0059] in, Is the measurement operator, used to observe the quantum state. It defines the measurement operation of the quantum state at time t. i，j Represents all possible quantum state basis |Ψ i (t)> and |Ψ j (t)> is summed, which means that the measurement operator It is constructed by weighting different ground states. ij (t) is the weight coefficient of the measurement operator, which represents the ground state |Ψ i (t)> and |Ψ j (t)>, this coefficient can change over time, reflecting the influence of different quantum states on the measurement results. i (t)> and |Ψ j (t)> is the ground state of the quantum system at time t, which represents the different possible states of the system. |Ψ′(t)> is the quantum state obtained after quantum intervention, and the measurement method is: using the measurement operator The adjusted quantum state |Ψ′(t)> is measured. The specific measurement steps are as follows:

[0060]

[0061] Through feedback from multiple measurement results, the system's intervention strategy is dynamically adjusted to ensure the security and stability of program behavior.

[0062] (7) System adaptive adjustment: According to the measurement feedback of the quantum state, the weight coefficient γ i Substitute into step (3) malicious behavior detection, substitute the control angle θ(t) into step (4) quantum state intervention, and substitute the random perturbation coefficient ∈(t) into step (5) random perturbation introduction to optimize the intervention effect.

[0063] Among them, the probability amplitude coefficients α(t) and β(t) in quantum state initialization are determined by the following nonlinear differential equations:

[0064]

[0065] in, and is the partial derivative of the system Hamiltonian H with respect to the complex conjugate of α(t) and β(t).

[0066] Among them, in the monitoring of quantum entanglement effects, the entanglement state coefficient is dynamically adjusted through an algorithm to respond to changes in the system state and achieve continuous monitoring of the target program behavior.

[0067] Among them, the quantum threshold function F(Ψ(t)) used in malicious behavior detection optimizes the threshold parameter γ through machine learning algorithm i , to adapt to the behavior patterns of different types of programs.

[0068] Among them, the angle parameter θ(t) of the control operator U(θ(t)) in quantum state intervention is adjusted in real time through a feedback mechanism to adapt to different malicious behavior patterns.

[0069] Among them, the random perturbation term R(∈(t)) used in the introduction of random perturbation is combined with a pseudo-random number generation algorithm based on chaos theory to enhance the system's anti-attack capability.

[0070] Among them, in multiple quantum state measurements and feedback, the measurement operator Dynamic adjustments are made based on real-time measurement data to ensure system stability under uncertain conditions.

[0071] Among them, the system's adaptive adjustment is achieved through a distributed computing architecture, which can process the initialization, monitoring, intervention and measurement of quantum states in parallel on multiple physical nodes to improve the system's response speed and processing capabilities.

[0072] The quantum state-based program behavior intervention method provided by the present invention solves the problems of insufficient accuracy in malicious behavior detection, monitoring challenges in dynamic environments, real-time intervention lags, and system predictability.

[0073] Example: Experimental verification of quantum state intervention system

[0074] Experimental objective: To verify the effectiveness of quantum state-based program behavior intervention methods in malicious behavior detection and intervention.

[0075] Experimental environment:

[0076] 1. Experimental platform: A high-performance computing server equipped with a quantum computing simulator (64-core CPU, 256GB of memory).

[0077] 2. Target program: A simulated version of OpenSSL that contains known vulnerabilities.

[0078] 3. Types of malicious behaviors: SQL injection, buffer overflow, cross-site scripting attack (XSS).

[0079] Experimental steps:

[0080] (1) Quantum state initialization: Quantumize the initial state of the target program to generate the quantum state |Ψ(t)>. Calculate the probability amplitude coefficients α(t) and β(t) to ensure the normalization condition.

[0081] (2) Monitoring of quantum entanglement effects: Use the quantum entangled state |Ψ(t)> for synchronous monitoring, which involves quantum correlations between systems.

[0082] (3) Malicious behavior detection: Apply the quantum threshold function F(Ψ(t)) for detection. Compare the detection accuracy of traditional detection methods with the quantum threshold method.

[0083] (4) Quantum state intervention: Use the quantum control operator U(θ(t)) to implement intervention and change the evolution path of the program's quantum state. Record the comparison data of the intervention success rate with the traditional method.

[0084] (5) Introduction of random perturbations: Introduce the random perturbation term R(∈(t)) and record the change in the attack success rate. Conduct comparative experiments with different perturbation coefficients to observe the impact of perturbations on system complexity.

[0085] (6) Multiple quantum state measurements and feedback: Conduct multiple quantum state measurements and use the measurement operator for dynamic feedback. Record the system stability data during the feedback process.

[0086] (7) System adaptive adjustment: Dynamically adjust the intervention strategy based on the feedback and record the system stability and adjustment effect.

[0087] Experimental data

[0088] Table 1: Detection accuracy and false alarm rate of malicious behaviors

[0089]

[0090] Table 2: Comparison of intervention success rate and failure rate

[0091]

[0092] Table 3: Impact of introducing random perturbations on the attack success rate

[0093] Perturbation coefficient ∈(t) Attack success rate (%) Reduction in attack success rate (%) Increase in system complexity (%) Average intervention delay (milliseconds) No perturbation 34 - 0 150 Low perturbation (∈ = 0.1) 21 13 11 140 Medium perturbation (∈ = 0.3) 13 21 22 130 High perturbation (∈ = 0.5) 9 25 35 120

[0094] Table 4: Improved system stability of quantum state measurement and feedback

[0095] Number of measurements Increase in system stability (%) Fluctuation range (%) Average measurement frequency (times / hour) Average feedback delay (seconds) 10 11 4 5 26 20 15 3 10 2.0 30 17 2 15 16

[0096] Table 5: Adaptive adjustment effect and control accuracy

[0097]

[0098] Table 6: Detection and intervention performance of different malicious behavior types

[0099]

[0100] Data analysis and validation

[0101] 1. Detection accuracy and false alarm rate: The quantum threshold method significantly improved the detection accuracy (87.67%) and reduced the false alarm rate (6.33%), verifying the effectiveness of quantum state intervention in improving detection accuracy.

[0102] 2. Intervention success rate and failure rate: The quantum state intervention method outperforms the traditional method in terms of intervention success rate (82.67%) and significantly reduces the intervention failure rate (17.33%), demonstrating the high efficiency of the quantum state intervention method in practical applications.

[0103] 3. The impact of random perturbations on system complexity: As the perturbation coefficient increases, the attack success rate decreases significantly (9%), while the system complexity increases and the intervention delay decreases, reflecting the robustness of the quantum state intervention method in complex environments.

[0104] 4. Quantum state measurement and feedback: The increase in the number of measurements leads to improved system stability, a smaller fluctuation range, and a reduction in intervention delay, verifying the positive impact of quantum state measurement and dynamic feedback on system stability.

[0105] 5. Adaptive adjustment effect: The dynamic adjustment strategy significantly improved system stability and intervention effect, reduced adjustment delay, and improved control accuracy, proving the effectiveness of the adaptive adjustment mechanism.

[0106] According to the experimental results, the present invention has the following innovations:

[0107] 1. Quantum state initialization: Traditional program behavior detection methods mainly rely on static analysis or dynamic analysis, detecting potential malicious behaviors through predefined rules or behavior patterns. These methods often struggle to effectively handle complex and polymorphic malicious codes, prone to false positives or false negatives. The present invention transforms the initial state of the target program through quantization, converting the program state into the quantum state |Ψ(t)>, enabling the precise calculation and control of the probability amplitudes of the program in the qubit state. This quantum state initialization process can describe more complex program states and behaviors, avoiding the limitations of traditional detection techniques when facing highly complex or variable program behaviors, and significantly improving the detection accuracy and flexibility.

[0108] 2. Monitoring of quantum entanglement effect: Existing program behavior monitoring methods usually independently monitor the behaviors of individual programs, lacking synchronous monitoring of the associated behaviors among multiple programs, especially difficult to capture potential linkage threats between different programs in distributed systems. The present invention introduces the quantum entanglement effect, entangling the quantum states of multiple programs to achieve synchronous monitoring of the behaviors of multiple programs. The existence of the entangled state enables the correlated analysis of the behaviors of different programs, greatly enhancing the detection ability for potential linkage threats in distributed systems. In contrast, it is very difficult for traditional technologies to achieve such efficient multi-program linkage monitoring.

[0109] 3. Quantum threshold function in malicious behavior detection: Existing malicious behavior detection relies on rule matching and feature analysis, usually difficult to deal with new or unknown malicious behaviors. And detection methods based on statistics and machine learning are prone to failure when facing new threats. The present invention defines a quantum threshold function F(Ψ(t)), using the expected value of the quantum state to evaluate the malicious behavior risk of the program. Different from existing technologies, the quantum threshold function can dynamically adjust and adapt to different types of program behavior patterns, greatly improving the detection ability for unknown malicious behaviors and reducing the false positive rate. Compared with traditional methods, the quantum threshold function has significant advantages in dealing with complex behavior patterns and non-linear relationships.

[0110] 4. Quantum state intervention and control: In traditional methods, once malicious behavior is detected, it is usually protected by terminating or isolating the program. However, this method often affects normal business operations and is difficult to effectively prevent malicious behavior before it occurs. The present invention, after detecting the tendency of malicious behavior, uses a quantum control operator U(θ(t)) to intervene in the quantum state of the target program. By adjusting the evolution path of the quantum state, it pre-intervenes and corrects the program behavior, preventing malicious behavior before it occurs. In contrast, traditional methods lack such a precise preventive intervention mechanism and are prone to affecting normal business.

[0111] 5. Introduction of random perturbations: The randomness introduced into existing defense systems is usually achieved through pseudo-random number generators. However, these pseudo-randomnesses are often easily predicted or exploited by attackers when facing advanced persistent threats (APTs). The present invention generates true quantum randomness by introducing a random perturbation term R(∈(t)) based on the quantum state, making the system's behavior more complex and unpredictable. This randomness not only enhances the system's ability to resist attacks, but also effectively prevents attackers from reversely inferring system behavior through repeated attempts. Compared with traditional pseudo-random number generation methods, quantum randomness has higher security and unpredictability.

[0112] 6. Multiple quantum state measurements and feedback: Traditional system protection strategies usually rely on fixed rules or strategy updates, which makes it difficult to respond to changes in program behavior in real time. In addition, the delay in measurement and feedback also makes it difficult for the system to adjust in time. This invention uses the measurement operator to measure the adjusted quantum state multiple times. Acquire real-time data and dynamically adjust the system's intervention strategy based on the measurement results. Compared to traditional technologies, the measurement and feedback mechanism of this invention can achieve real-time response and optimize control parameters based on the latest quantum state data, ensuring that the system can quickly adjust to unknown threats and maintain optimal protection.

[0113] 7. System Adaptive Adjustment: Existing program behavior protection systems mostly use preset static rules or parameters, which lack flexibility and are slow to respond to dynamically changing threat environments. The system adaptive adjustment mechanism of the present invention uses a distributed computing architecture to achieve the control parameters θ(t), weight coefficient γ i and dynamic adjustment of the random perturbation coefficient ∈(t). Compared with traditional technologies, the present invention can quickly adapt to changing threat environments, improve the system's response capabilities and processing efficiency, and accelerate response speed through parallel computing, thereby improving overall protection performance.

[0114] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A program behavior intervention method based on quantum states, characterized in that Including the following steps: (1) Quantum state initialization: Quantize the initial state of the target program to construct the quantum state of the program , which is represented by the following mathematical expression: ； Among them, and are functions of time, representing the probability amplitudes of the program in different qubit states, satisfying the normalization condition ; ; (2) Quantum entanglement effect monitoring: By introducing the quantum entanglement effect, entangling the quantum states of multiple systems, and realizing the synchronous monitoring of the behaviors of multiple programs. The mathematical expression of the entangled state is: ； This expression is used to describe the quantum correlation between systems to ensure the simultaneous monitoring of the behaviors of multiple programs; (3)Malicious behavior detection: Define a quantum threshold function to detect possible malicious behaviors of the program. This quantum threshold function is based on the following formula: ； Among them, is the weight coefficient, is the Hamiltonian operator, which is used to calculate the expected value of the program quantum state to evaluate the risk of malicious behavior; (4) Quantum state intervention: After detecting malicious behavior tendencies, use quantum control operators to intervene in the quantum state of the target program and change its quantum state evolution path. The mathematical expression is: ； Among them, is the control angle, and the quantum state of the program is adjusted through this operator to prevent malicious behavior from occurring; (5) Introduction of random perturbation: A random perturbation term is introduced during the intervention process , and the expression of this random perturbation term is: ； Among them, is a random perturbation coefficient, is a random state parameter, which increases the complexity of the system through perturbation, making it difficult for attackers to predict and utilize; (6) Multi - quantum state measurement and feedback: Perform multiple measurements on the adjusted quantum state, and the measurement operator is defined as: ； By feeding back the results of multiple measurements, the intervention strategy of the system is dynamically adjusted to ensure the safety and stability of program behavior, where is the weight coefficient of the measurement operator, and are respectively the ground states of the quantum system at time t; (7) System adaptive adjustment: Dynamically adjust the control angle according to the measurement feedback of the quantum state , weight coefficient and random perturbation coefficient , then substitute the weight coefficient into the malicious behavior detection in step (3), substitute the control angle into the quantum state intervention in step (4), and substitute the random perturbation coefficient into the random perturbation introduction in step (5) to optimize the intervention effect.

2. The program behavior intervention method based on quantum states according to claim 1, wherein The probability amplitude coefficients in the quantum state initialization in step (1) and are determined by the following non-linear differential equation: ； wherein, and is the system Hamiltonian the partial derivative with respect to and the complex conjugate of.

3. The method for intervening in program behavior based on quantum states according to claim 1, wherein In the malicious behavior detection of step (3), the quantum threshold function used Optimize the weight coefficients through machine learning algorithms , to adapt to the behavior patterns of different types of programs.

4. The program behavior intervention method based on quantum states according to claim 1, characterized in that The control operator in the quantum state intervention of step (4) The control angle Is adjusted in real time through a feedback mechanism to adapt to different malicious behavior patterns.

5. The program behavior intervention method based on quantum states according to claim 1, characterized in that In the multiple quantum state measurements and feedback in step (6), the measurement operator is dynamically adjusted based on real-time measurement data to ensure the stability of the system under uncertain conditions.

6. The program behavior intervention method based on quantum states according to claim 1, wherein The adaptive adjustment of the system in step (7) is achieved through a distributed computing architecture, which can parallelly process the initialization, monitoring, intervention, and measurement of quantum states on multiple physical nodes to improve the response speed and processing capacity of the system.

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