Program behavior intervention method based on quantum state

Abstract

According to the program behavior intervention method based on the quantum state, the target program state is quantized, the quantum state is generated, synchronous monitoring of multiple program behaviors is achieved through the quantum entanglement effect, and the malicious behavior tendency of the program is detected by defining the quantum threshold function; the program quantum state is intervened by using a quantum control operator, so that the program state is adjusted and malicious behaviors are prevented; in addition, a random disturbance term is introduced to increase system complexity, and an intervention strategy is dynamically adjusted through multiple quantum state measurement and feedback, so that control parameters are optimized. According to the method, the problems of insufficient detection precision and weak defense capability when a traditional program behavior detection technology is used for processing complex program behaviors are effectively solved, the safety and the stability of the system are remarkably improved, and the detection and defense capability on complex malicious behaviors is enhanced.

Description

Technical Field

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

[0002] At present, with the rapid development of information technology, various computer programs are widely used in key areas such as industrial control, financial transactions, and data processing. However, with the increase in program complexity, the threat of malicious behavior is also escalating, posing serious hidden dangers to network security. Traditional program behavior detection and protection methods often show certain limitations when facing complex malicious behaviors, making it 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 warning and protection. At the same time, traditional protection measures are mostly post-detection and response, lacking the ability to actively intervene in advance, resulting in the possibility of irreversible damage to the system once malicious behavior occurs.

[0004] In addition, the existing security protection mechanism is difficult to effectively coordinate and monitor the related behaviors between different systems when dealing with distributed systems and multi-program environments. This limitation makes it possible for malicious behaviors to spread through coordinated attacks between systems, further exacerbating the difficulty of protection and failing to meet the security needs in modern complex network environments. Summary of the invention

[0005] In order to solve the problems of insufficient accuracy in malicious behavior detection, monitoring challenges in dynamic environments, lag in real-time intervention, and system predictability, this paper proposes a program behavior intervention method based on quantum states. This method improves the accuracy of malicious behavior detection by quantizing program states, using quantum entanglement for synchronous monitoring, and using quantum threshold functions, and combines quantum control operators for real-time intervention. In addition, by introducing random perturbations to increase system complexity, performing multiple quantum state measurements, and dynamically feedback to adjust intervention strategies, efficient adaptive regulation and enhanced anti-attack capabilities are achieved.

[0006] A quantum state-based program behavior intervention method comprises the following steps:

[0007] (1) Quantum state initialization: The initial state of the target program is quantized to construct the quantum state of the program |Ψ(t)>, 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 qubit 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 to achieve synchronous monitoring of multiple program behaviors. The mathematical expression of the entangled state is:

[0011]

[0012] This expression is used to describe the quantum correlation between systems to ensure the behavior of multiple programs can be monitored simultaneously;

[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, through which the quantum state of the program is adjusted 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 feedback from multiple measurement results, the system intervention strategy is dynamically adjusted to ensure the security and stability of program behavior.

[0025] (7) System adaptive adjustment: Dynamically adjust the control angle θ(t) and weight coefficient γ according to the measurement feedback of the quantum state i and random perturbation coefficient ∈(t), and then 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.

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

[0027]

[0028] in, and is the partial derivative of the system Hamiltonian H with respect to the complex conjugate of α(t) and β(t).

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

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

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

[0032] The present invention has the following advantages:

[0033] 1. High-precision malicious behavior detection: Through quantum state initialization and 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 capability: Apply quantum control operators to achieve instant adjustment of the program quantum state and quickly respond to potential threats, significantly reducing the response delay in traditional protection measures and improving the system's real-time protection capabilities.

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

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

[0037] 5. Intelligent adaptive regulation: The dynamic adjustment mechanism based on quantum state measurement feedback realizes the 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 It is a flow chart of a program behavior intervention method based on quantum state according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the 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 creative work are 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: The initial state of the target program is quantized to construct the quantum state of the program |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 qubit 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, and |11> represents that both qubits are in the |1> state. The expression |00>+|11> represents the superposition state of two qubits. Since the probability amplitude of the quantum state needs to satisfy the normalization condition, that is, the sum of the probabilities of all possible states must be equal to 1, we introduce the normalization factor This expression is used to describe the quantum correlation between systems to ensure that the behavior of multiple programs can be monitored simultaneously. The above formula is the quantum state operation to obtain the entangled state.

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

[0048]

[0049] Among them, γ i is the weight coefficient, is a Hamiltonian operator, which is used to calculate the expected value of the program quantum state to assess the risk of malicious behavior. The quantum state threshold when the system is operating normally 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 the corresponding defense mechanism needs to be triggered or intervention needs to be performed.

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

[0051]

[0052] Among them, θ(t) is the control angle, and the quantum state of the program is adjusted through this operator to prevent malicious behavior from occurring.

[0053] (5) Introduction of random disturbance: A random disturbance term R(∈(t)) is introduced during the intervention process. The expression of this disturbance 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 bases |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 impact 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 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 the quantum state-based program behavior intervention method in malicious behavior detection and intervention.

[0075] Experimental environment:

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

[0077] 2. Target program: OpenSSL emulated version, containing known vulnerabilities.

[0078] 3. Malicious behavior types: SQL injection, buffer overflow, cross-site scripting (XSS).

[0079] Experimental steps:

[0080] (1) Quantum state initialization: Quantize 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: Using the quantum entangled state |Ψ(t)> for synchronous monitoring, it 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 and quantum threshold methods.

[0083] (4) Quantum state intervention: Use the quantum control operator U(θ(t)) to implement intervention and change the evolution path of the program quantum state. Record the intervention success rate and compare it with the traditional method.

[0084] (5) Introduction of random perturbations: Introduce the random perturbation term R(∈(t)) and record the changes 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: Perform multiple quantum state measurements and use the measurement operator Conduct dynamic feedback and record system stability data during the feedback process.

[0086] (7) System adaptive regulation: Dynamically adjust intervention strategies based on feedback and record system stability and regulation effects.

[0087] Experimental data

[0088] Table 1: Malicious behavior detection accuracy and false alarm rate

[0089]

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

[0091]

[0092] Table 3: Effect of random perturbation on attack success rate

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

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

[0095] Number of measurements System stability improvement (%) 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 is superior to the traditional method in terms of intervention success rate (82.67%) and significantly reduces the intervention failure rate (17.33%), showing 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%). At the same time, the system complexity increases and the intervention delay decreases, which reflects 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 reduced intervention delays, verifying the positive impact of quantum state measurement and dynamic feedback on system stability.

[0105] 5. Adaptive adjustment effect: The dynamic adjustment strategy significantly improves the system stability and intervention effect, reduces the adjustment delay, and improves the control accuracy, which proves 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 to detect potential malicious behaviors through predefined rules or behavior patterns. These methods are often difficult to deal with effectively when faced with complex, polymorphic malicious code, and are prone to false positives or omissions. The present invention quantizes the initial state of the target program and converts the program state into a quantum state |Ψ(t)>, so that the probability amplitude of the program in the quantum bit state can be accurately calculated and controlled. This quantum state initialization process can describe more complex program states and behaviors, avoiding the limitations of traditional detection technology when faced with highly complex or changeable program behaviors, and significantly improving the accuracy and flexibility of detection.

[0108] 2. Monitoring of quantum entanglement effects: Existing program behavior monitoring methods usually monitor the behavior of a single program independently, lacking synchronous monitoring of the related behaviors of multiple programs, especially in distributed systems, where it is difficult to capture potential linkage threats between different programs. The present invention introduces the quantum entanglement effect, entangling the quantum states of multiple programs together to achieve synchronous monitoring of the behaviors of multiple programs. The existence of entangled states enables the behaviors of different programs to be correlated and analyzed, greatly enhancing the ability to detect potential linkage threats in distributed systems. In contrast, it is 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, which is 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)) and uses the expected value of the quantum state to evaluate the malicious behavior risk of the program. Unlike the prior art, the quantum threshold function can dynamically adjust and adapt to different types of program behavior patterns, greatly improving the detection ability of unknown malicious behaviors and reducing the false alarm rate. Compared with traditional methods, the quantum threshold function has significant advantages in dealing with complex behavior patterns and nonlinear relationships.

[0110] 4. Quantum state intervention and control: In traditional methods, once malicious behavior is detected, protection is usually carried out by terminating or isolating the program. However, this method often affects normal business operations and it is difficult to effectively prevent malicious behavior before it occurs. After detecting malicious behavior tendencies, the present invention uses the 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, program behavior is intervened and corrected in advance, so that malicious behavior can be curbed before it occurs. In contrast, traditional methods lack such precise preventive intervention mechanisms and are prone to affecting normal business.

[0111] 5. Introduction of random perturbations: The randomness introduced in existing defense systems is usually achieved through pseudo-random number generators. However, these pseudo-randomness are often easily predicted or exploited by attackers when facing advanced persistent threats (APTs). The present invention generates real quantum randomness by introducing a random perturbation term R(∈(t)) based on the quantum state, making the behavior of the system more complex and unpredictable. This randomness not only enhances the system's anti-attack capability, but also effectively prevents attackers from reversely inferring the system's behavior through repeated probing. 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 are 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 measures the adjusted quantum state multiple times and uses the measurement operator Acquire real-time data and dynamically adjust the system's intervention strategy based on the measurement results. Compared with traditional technologies, the measurement and feedback mechanism of the present invention can achieve real-time response and optimize control parameters based on the latest quantum state data, thereby ensuring that the system can quickly adjust and maintain the best protection state when facing unknown threats.

[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 disturbance coefficient ∈(t). Compared with traditional technologies, the present invention can quickly adapt to the changing threat environment, improve the system's response capability and processing efficiency, and accelerate the response speed through parallel computing to improve the overall protection performance.

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

Claims

1. A method for program behavior intervention based on quantum state, characterized in that: The following steps are involved: (1) Quantum state initialization: The initial state of the target program is quantized to construct the quantum state of the program |Ψ(t)>, which is represented by the following mathematical expression: |Ψ(t)>=α(t)|0>+β(t)|1> Among them, α(t) and β(t) are functions of time t, representing the probability amplitude of the program in different qubit states, satisfying the normalization condition |α(t)| 2 +|β(t)| 2 =1; (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 mathematical expression of the entangled state is: This expression is used to describe the quantum correlation between systems to ensure the behavior of multiple programs can be monitored simultaneously; (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: 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; (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: Among them, θ(t) is the control angle, through which the quantum state of the program is adjusted to prevent malicious behavior; (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: R(∈(t))=∈(t)·|η(t)> 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; (6) Multiple quantum state measurements and feedback: Multiple measurements are performed on the adjusted quantum state, and the measurement operator Defined as: Through multiple measurement result feedback, the system intervention strategy is dynamically adjusted to ensure the safety and stability of program behavior. ij (t) is the weight coefficient of the measurement operator, |Ψ i (t)> and |Ψ j (t)> are the ground states of the quantum system at time t; (7) System adaptive adjustment: Dynamically adjust the control angle θ(t) and weight coefficient γ according to the measurement feedback of the quantum state i and random perturbation coefficient ∈(t), and then 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.

2. The method for quantum state-based program behavior intervention according to claim 1, characterized in that: The probability amplitude coefficients α(t) and β(t) in the quantum state initialization in step (1) are determined by the following nonlinear differential equations: in, and is the partial derivative of the system Hamiltonian H with respect to the complex conjugate of α(t) and β(t).

3. The method for quantum state-based program behavior intervention according to claim 1, characterized in that: In the malicious behavior detection of step (3), the quantum threshold function F(Ψ(t)) used is optimized by the machine learning algorithm to obtain the weight coefficient γ i , to adapt to the behavior patterns of different types of programs.

4. The method for quantum state-based program behavior intervention according to claim 1, characterized in that: The control angle θ(t) of the control operator U(θ(t)) in the quantum state intervention of step (4) is adjusted in real time through a feedback mechanism to adapt to different malicious behavior modes.

5. The method for quantum state-based program behavior intervention according to claim 1, characterized in that: In the multiple quantum state measurements and feedback in step (6), the measurement operator Dynamic adjustments are made based on real-time measurement data to ensure system stability under uncertain conditions.

6. The method for quantum state-based program behavior intervention according to claim 1, characterized in that: The adaptive adjustment of the system in step (7) 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 response speed and processing power of the system.

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