Power system network attack detection method and device based on quantum decision information and computer equipment

Abstract

The invention relates to a power system network attack detection method and device based on quantum decision information, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring an initial quantum state of a power system network, and constructing a quantum interference operation matrix of the power system network; adjusting the initial quantum state according to the quantum interference operation matrix to obtain an adjusted quantum state as a state detection quantum state; the state detection quantum state comprises a normal state quantum state and an attack state quantum state; obtaining power system attack information corresponding to the state detection quantum state according to the current power access data of the power system network and the state detection quantum state; identifying target state information of the power system network according to power system attack information corresponding to the state detection quantum state; the target state information comprises normal state information or attack state information. By adopting the method, the network attack detection accuracy of the power system can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power system network security, and in particular, to a power system network attack detection method, device, computer device, computer-readable storage medium, and computer program product based on quantum decision information. Background Art

[0002] With the development of the digitization and intelligentization of power systems, their dependence on communication networks has gradually increased, but at the same time, they are also facing increasingly severe network security threats. The frequent occurrence of network attacks such as distributed denial of service attacks (DDoS), data tampering, and network intrusion has brought huge challenges to the stable operation of power systems. These attacks may not only cause the interruption of the operation of key devices but also trigger a series of security problems such as unstable power supply and data leakage.

[0003] Traditional network attack detection methods mainly rely on statistical analysis, machine learning, and rule-based detection algorithms. These methods have achieved certain results in the recognition of known attack patterns, but in the face of high-dimensional non-linear data and dynamic and uncertain environments in power systems, there are still problems with low detection accuracy. Summary of the Invention

[0004] Based on this, it is necessary to provide a power system network attack detection method, device, computer device, computer-readable storage medium, and computer program product based on quantum decision information that can improve the accuracy of power system network attack detection for the above technical problems.

[0005] In a first aspect, the present application provides a power system network attack detection method based on quantum decision information, including:

[0006] Obtain the initial quantum state of the power system network and construct the quantum interference operation matrix of the power system network;

[0007] According to the quantum interference operation matrix, adjust the initial quantum state to obtain an adjusted quantum state as the state detection quantum state; the state detection quantum state includes a normal state quantum state and an attack state quantum state;

[0008] According to the current power access data of the power system network and the state detection quantum state, obtain the power system attack information corresponding to the state detection quantum state;

[0009] According to the power system attack information corresponding to the state detection quantum state, identify the target state information of the power system network; the target state information includes normal state information or attack state information.

[0010] In one embodiment, obtaining the power system attack information corresponding to the state detection quantum state according to the current power access data of the power system network and the state detection quantum state includes:

[0011] Obtaining a likelihood value of the state detection quantum state relative to the current power access data according to the current power access data and the state detection quantum state;

[0012] Obtaining a prior probability of the state detection quantum state for this detection;

[0013] Determining the power system attack information corresponding to the state detection quantum state according to the likelihood value and the prior probability.

[0014] In one embodiment, obtaining the prior probability of the state detection quantum state for this detection includes:

[0015] In the case where this detection is the first detection, obtaining a preset prior probability of the state detection quantum state as the prior probability of the state detection quantum state for this detection;

[0016] In the case where this detection is not the first detection, obtaining the power system attack information corresponding to the state detection quantum state obtained in the previous detection as the prior probability of the state detection quantum state for this detection.

[0017] In one embodiment, identifying the target state information of the power system network according to the power system attack information corresponding to the state detection quantum state includes:

[0018] Determining a target state detection quantum state from the state detection quantum states according to the power system attack information;

[0019] Taking the state information corresponding to the target state detection quantum state as the target state information of the power system network.

[0020] In one embodiment, obtaining the initial quantum state of the power system network includes:

[0021] Generating a plurality of quantum state setting vectors according to the historical power access data of the power system network; the historical power access data includes normal data and attack data;

[0022] Obtaining the initial quantum state according to the plurality of quantum state setting vectors.

[0023] In one embodiment, obtaining the initial quantum state according to the plurality of quantum state setting vectors includes:

[0024] Set vectors according to the multiple quantum states, and generate corresponding multiple basic quantum states;

[0025] Generate multiple composite quantum states according to the multiple basic quantum states;

[0026] Integrate the multiple basic quantum states and the multiple composite quantum states into the initial quantum state.

[0027] In a second aspect, the present application also provides a power system network attack detection device based on quantum decision information, including:

[0028] An initial processing module, configured to obtain the initial quantum state of the power system network and construct a quantum interference operation matrix of the power system network;

[0029] A quantum adjustment module, configured to adjust the initial quantum state according to the quantum interference operation matrix to obtain an adjusted quantum state as a state detection quantum state; the state detection quantum state includes a normal state quantum state and an attack state quantum state;

[0030] An information determination module, configured to obtain power system attack information corresponding to the state detection quantum state according to the current power access data of the power system network and the state detection quantum state;

[0031] A state determination module, configured to identify target state information of the power system network according to the power system attack information corresponding to the state detection quantum state; the target state information includes normal state information or attack state information.

[0032] In a third aspect, the present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0033] Obtain the initial quantum state of the power system network and construct a quantum interference operation matrix of the power system network;

[0034] Adjust the initial quantum state according to the quantum interference operation matrix to obtain an adjusted quantum state as a state detection quantum state; the state detection quantum state includes a normal state quantum state and an attack state quantum state;

[0035] Obtain power system attack information corresponding to the state detection quantum state according to the current power access data of the power system network and the state detection quantum state;

[0036] Identify target state information of the power system network according to the power system attack information corresponding to the state detection quantum state; the target state information includes normal state information or attack state information.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0038] Obtain the initial quantum state of the power system network and construct the quantum interference operation matrix of the power system network;

[0039] According to the quantum interference operation matrix, adjust the initial quantum state to obtain an adjusted quantum state as the state detection quantum state; the state detection quantum state includes a normal state quantum state and an attack state quantum state;

[0040] According to the current power access data of the power system network and the state detection quantum state, obtain the power system attack information corresponding to the state detection quantum state;

[0041] According to the power system attack information corresponding to the state detection quantum state, identify the target state information of the power system network; the target state information includes normal state information or attack state information.

[0042] In a fifth aspect, the present application further provides a computer program product, including a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0043] Obtain the initial quantum state of the power system network and construct the quantum interference operation matrix of the power system network;

[0044] According to the quantum interference operation matrix, adjust the initial quantum state to obtain an adjusted quantum state as the state detection quantum state; the state detection quantum state includes a normal state quantum state and an attack state quantum state;

[0045] According to the current power access data of the power system network and the state detection quantum state, obtain the power system attack information corresponding to the state detection quantum state;

[0046] According to the power system attack information corresponding to the state detection quantum state, identify the target state information of the power system network; the target state information includes normal state information or attack state information.

[0047] The above-mentioned power system network attack detection method, device, computer equipment, computer-readable storage medium and computer program product based on quantum decision information. First, obtain the initial quantum state of the power system network and construct the quantum interference operation matrix of the power system network. By generating the initial quantum state based on the historical access data of the power system, the normal state and various attack states of the system can be comprehensively characterized. The initial quantum state represents the normal state and attack states in the form of quantum states, providing a multi-dimensional and high-precision initial state basis for subsequent detection. The quantum interference operation matrix can make the quantum state more flexibly reflect the system characteristics by adjusting the amplitude and phase of the quantum state, enhancing the sensitivity to complex states and effectively improving the accuracy and expression ability of system state initialization. Then, according to the quantum interference operation matrix, adjust the initial quantum state to obtain the adjusted quantum state as the state detection quantum state. Among them, the state detection quantum state includes the normal state quantum state and the attack state quantum state. The adjustment process of the quantum interference operation matrix on the initial quantum state can highlight the characteristics of different states, enabling the state detection quantum state to accurately reflect the current system operation state. The state detection quantum state can clearly distinguish the characteristics of different attacks and at the same time has the ability of dynamic change, adapting to the operation environment and abnormal conditions of complex power networks, significantly improving the discrimination ability for composite attack modes. Then, according to the current power access data of the power system network and the state detection quantum state, obtain the power system attack information corresponding to the state detection quantum state. By combining the current power access data with the state detection quantum state and calculating the likelihood value and prior probability, the power system attack information can be dynamically generated, reflecting the attack characteristics and their possibilities in the current detection. Utilizing the high-dimensional feature representation ability of the quantum state, the abnormal changes in the current data can be effectively captured, improving the detection real-time performance. Finally, according to the power system attack information corresponding to the state detection quantum state, identify the target state information of the power system network. Among them, the target state information includes normal state information or attack state information. According to the attack information matched by the state detection quantum state, quickly identify the target state detection quantum state and judge its corresponding target state information, including normal state or attack state, which can efficiently distinguish the types and intensities of attacks (such as mild, moderate, severe), and even accurately separate different attack states in the case of composite attack scenarios, providing accurate state recognition results for the system, while reducing manual intervention and improving the automation degree and decision-making efficiency of the system. In the above method, through the construction of the initial quantum state, the adjustment of the quantum interference operation, the combination of real-time data and the state detection quantum state, and the identification of the target state information, the problems of low accuracy and poor adaptability of traditional detection methods in the high-dimensional non-linear data environment of the power system are solved, and the overall ability of the system to distinguish various attack states is improved, realizing the organic combination of real-time performance, robustness and efficiency.In addition, through the high-dimensional representation and dynamic adjustment of quantum states, the solution has the ability to quickly respond to complex abnormal scenarios, providing higher detection accuracy and decision-making reliability for the safe operation of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a schematic flowchart of a method for detecting power system network attacks based on quantum decision information in an embodiment;

[0050] Figure 2 It is a schematic flowchart of the steps for determining the power system attack information corresponding to the state detection quantum state in an embodiment;

[0051] Figure 3 It is a schematic flowchart of a method for detecting power system network attacks based on quantum decision information in another embodiment;

[0052] Figure 4 It is a comparison chart of the detection accuracy rates of multiple detection methods in an embodiment;

[0053] Figure 5 It is a comparison chart of the detection response times of multiple detection methods in an embodiment;

[0054] Figure 6 It is a comparison chart of the adaptability of multiple detection methods under noise interference in an embodiment;

[0055] Figure 7 It is a comparison chart of the detection adaptability of multiple detection methods under different attack modes in an embodiment;

[0056] Figure 8 It is a structural block diagram of a device for detecting power system network attacks based on quantum decision information in an embodiment;

[0057] Figure 9 It is an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] In order to make the objectives, technical solutions, and advantages of the present application clearer, the following further elaborates on the present application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] In one embodiment, as Figure 1 shown, a power system network attack detection method based on quantum decision information is provided. In this embodiment, taking the application of this method to a terminal as an example, it can be understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablet computers, etc. The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. In this embodiment, the method includes the following steps:

[0060] Step S101, obtain the initial quantum state of the power system network and construct a quantum interference operation matrix for the power system network.

[0061] Among them, the initial quantum state is a quantum state representation generated through the historical access data of the power system network, which can describe the normal state of the system and various possible attack states. Each attack state also includes different attack intensities such as mild, moderate, and severe. The quantum interference operation matrix is an operation matrix used to adjust the amplitude and phase of the initial quantum state, enhancing the characterization ability of the quantum state for specific states through quantum computing characteristics, so that it can more sensitively reflect the differences in different states of the power system.

[0062] Exemplarily, the terminal extracts feature data from the historical access data of the power system. These feature data include normal operation data (such as load current, voltage frequency, etc.) and known attack pattern data (such as traffic characteristics of DDoS attacks, parameter deviations of data tampering, etc.). The terminal preprocesses the extracted feature data, including denoising, standardization, and normalization operations, to generate a quantum state setting vector for initialization. Subsequently, the terminal maps the quantum state setting vector into the quantum state space through a quantum state generation model to generate basic quantum states (such as normal states, attack states, etc.). On this basis, the terminal uses quantum superposition and entanglement operations to generate a composite quantum state, and integrates the basic quantum state and the composite quantum state to form a complete initial quantum state. At the same time, the terminal constructs a quantum interference operation matrix according to the state characteristics and detection requirements of the power system. The parameter settings of this matrix combine the distribution characteristics of different states in historical data, and can achieve precise adjustment of the amplitude and phase of the initial quantum state to generate attack quantum states of different attack degrees. For example, for a mild attack state, the adjustment amplitude of the quantum interference matrix is small, while for a severe attack state, the adjustment amplitude is large to amplify the characteristics of the abnormal state.

[0063] Step S102, adjust the initial quantum state according to the quantum interference operation matrix to obtain an adjusted quantum state, which is used as the state detection quantum state.

[0064] Among them, the state detection quantum state includes the normal state quantum state and the attack state quantum state.

[0065] Among them, the quantum interference operation matrix is a mathematical tool for adjusting the amplitude and phase of the initial quantum state. Through the interference characteristics in quantum computing, the target features in the initial quantum state are enhanced or suppressed to meet the state detection requirements of the power system. The state detection quantum state refers to the quantum state adjusted by the quantum interference operation, which is used to represent the current state of the system, including the normal state quantum state and the attack state quantum state.

[0066] Exemplarily, the terminal performs an adjustment operation on the initial quantum state based on the generated initial quantum state and the quantum interference operation matrix. First, the terminal loads the initial quantum state, whose structure includes the normal state quantum state Ψ 0 and the attack state quantum state (such as Ψ 1 , Ψ 2 , Ψ 3 ), etc. The amplitude and phase of each quantum state encode the characteristic weights of the corresponding state. Subsequently, the terminal applies the quantum interference operation matrix to the initial quantum state. Specifically, the terminal uses the interference matrix to operate on the amplitude and phase of the quantum state of the attack state. The interference matrix amplifies the attack features, such as enhancing the characteristic signals of communication traffic anomalies, data deviations, and illegal access; while for the normal state quantum state, no adjustment is made to ensure the integrity of the normal state features. In addition, the parameters of the interference matrix are also dynamically set based on the different intensities (mild, moderate, severe) of the attack state: mild attack state: small-scale adjustment, slightly enhancing the abnormal features; moderate attack state: medium-scale adjustment, significantly amplifying the abnormal features; severe attack state: large-scale adjustment, greatly highlighting the abnormal features. Through the result of the interference operation, the terminal generates the adjusted quantum state, that is, the state detection quantum state. The state detection quantum state can not only distinguish the normal state and the attack state, but also reflect the attack type and intensity through the distribution of the amplitude and phase.

[0067] Step S103, according to the current power access data of the power system network and the state detection quantum state, obtain the power system attack information corresponding to the state detection quantum state.

[0068] Exemplarily, the terminal first obtains the current power access data from the real-time monitoring module. The terminal matches the data with the state detection quantum state one by one, evaluates the matching degree between the data and different quantum states by calculating the likelihood value. Subsequently, the terminal combines the prior probability and the likelihood value of the state detection quantum state, and uses the Bayesian formula to calculate the posterior probability of each state detection quantum state, that is, the power system attack information. These probabilities describe the degree to which the current power access data supports different state detection quantum states.

[0069] Step S104: Based on the power system attack information corresponding to the state detection quantum state, identify the target state information of the power system network.

[0070] Among them, the target state information includes normal state information or attack state information.

[0071] Exemplarily, based on the obtained posterior probability distribution of the state detection quantum state, the terminal analyzes each state detection quantum state to identify the target state information of the power system network. First, the terminal finds the target state detection quantum state corresponding to the maximum posterior probability from the posterior probability distribution, and the terminal identifies the target state information of the power system according to the state information corresponding to the target state detection quantum state. Subsequently, the terminal outputs the target state information in a structured manner, including: state category: normal or attack; type of attack: such as DDoS, data tampering, network intrusion, etc.; attack intensity: mild, moderate, severe. In the case of a composite attack scenario, if the posterior probabilities of multiple attack states are close, the terminal will analyze the information of the state detection quantum state with the second-highest posterior probability and combine it with the state detection quantum state with the maximum posterior probability to identify the composite attack type. For example, the system may be simultaneously under a moderate DDoS attack and a mild network intrusion attack.

[0072] In the above-mentioned power system network attack detection method based on quantum decision information, first, the initial quantum state of the power system network is obtained, and the quantum interference operation matrix of the power system network is constructed. By generating the initial quantum state based on the historical access data of the power system, the normal state and various attack states of the system can be comprehensively characterized. The initial quantum state represents the normal state and attack states in the form of quantum states, providing a multi-dimensional and high-precision initial state basis for subsequent detection. The quantum interference operation matrix can make the quantum state more flexibly reflect the system characteristics by adjusting the amplitude and phase of the quantum state, enhancing the sensitivity to complex states and effectively improving the accuracy and expression ability of system state initialization. Then, according to the quantum interference operation matrix, the initial quantum state is adjusted to obtain the adjusted quantum state as the state detection quantum state. Among them, the state detection quantum state includes the normal state quantum state and the attack state quantum state. The adjustment process of the quantum interference operation matrix on the initial quantum state can highlight the characteristics of different states, enabling the state detection quantum state to accurately reflect the current system operation state. The state detection quantum state can clearly distinguish the characteristics of different attacks and also has the ability of dynamic change, adapting to the operation environment and abnormal situations of complex power networks, significantly enhancing the discrimination ability for composite attack modes. Then, according to the current power access data of the power system network and the state detection quantum state, the power system attack information corresponding to the state detection quantum state is obtained. By combining the current power access data with the state detection quantum state and calculating the likelihood value and prior probability, the power system attack information can be dynamically generated, reflecting the attack characteristics and their possibilities in the current detection. Utilizing the high-dimensional feature representation ability of the quantum state, the abnormal changes in the current data can be effectively captured, improving the detection real-time performance. Finally, according to the power system attack information corresponding to the state detection quantum state, the target state information of the power system network is identified. Among them, the target state information includes normal state information or attack state information. According to the attack information matched by the state detection quantum state, the target state detection quantum state can be quickly identified, and its corresponding target state information, including normal state or attack state, can be judged. It can efficiently distinguish the types and intensities of attacks (such as mild, moderate, and severe), and even accurately separate different attack states in the composite attack scenario, providing accurate state recognition results for the system, while reducing manual intervention and improving the automation degree and decision-making efficiency of the system. In the above method, through the construction of the initial quantum state, the adjustment of the quantum interference operation, the combination of real-time data and the state detection quantum state, and the identification of the target state information, the problems of low accuracy and poor adaptability of traditional detection methods in the high-dimensional non-linear data environment of the power system are solved, overall enhancing the system's ability to distinguish various attack states and realizing the organic combination of real-time performance, robustness, and efficiency. In addition, through the high-dimensional characterization and dynamic adjustment of the quantum state, the solution has the ability to quickly respond to complex abnormal scenarios, providing higher detection accuracy and decision-making reliability for the safe operation of the power system.

[0073] In an exemplary embodiment, as Figure 2 shown, the above step S103 obtains power system attack information corresponding to the state detection quantum state according to the current power access data and state detection quantum state of the power system network, and can also be implemented through the following steps:

[0074] Step S201: Obtain the likelihood value of the state detection quantum state relative to the current power access data according to the current power access data and state detection quantum state;

[0075] Step S202: Obtain the prior probability of the state detection quantum state for this detection;

[0076] Step S203: Determine the power system attack information corresponding to the state detection quantum state according to the likelihood value and prior probability.

[0077] Among them, the likelihood value refers to the matching degree between the state detection quantum state and the current power access data, and is used to evaluate the support degree of historical data for the current state detection quantum state. The prior probability is the possibility estimation of each state detection quantum state before the current detection, and comes from the system initialization setting or the posterior probability of the previous detection period. The power system attack information is the result calculated based on the posterior probability of the current power access data and state detection quantum state, including the probability distribution of each state detection quantum state, and reflects the attack type and intensity.

[0078] Exemplarily, the terminal obtains the current power access data from the real-time monitoring module, including characteristic parameters such as communication traffic, voltage frequency deviation, and load fluctuation within the current period. The terminal calculates the likelihood value of each state detection quantum state relative to the current power access data one by one through the feature matching model between the quantum state and the data. Subsequently, the terminal obtains the prior probability of the state detection quantum state for this detection. Next, the terminal combines the current power access data, likelihood value, and prior probability, and uses the Bayesian formula to calculate the posterior probability of each state detection quantum state, that is, obtains the power system attack information corresponding to the state detection quantum state.

[0079] In this embodiment, by combining the current power access data and state detection quantum state, and dynamically calculating the posterior probability using Bayesian inference, the real-time judgment and high-precision recognition of the current state of the power system are realized. Through the matching of quantum state features and probability distribution analysis, the system can accurately distinguish the normal state and various attack states, further refine the attack type and intensity, and at the same time have the ability to identify composite attacks. In addition, this method shows good robustness in a high-noise environment, effectively reducing false alarms and missed alarms, and providing accurate and reliable data support for subsequent target state recognition and security protection.

[0080] In an exemplary embodiment, the above step S202 of obtaining the prior probability of the state detection quantum state for this detection further includes: in the case where this detection is the first detection, obtaining the preset prior probability of the state detection quantum state as the prior probability of the state detection quantum state for this detection; in the case where this detection is not the first detection, obtaining the power system attack information corresponding to the state detection quantum state obtained in the previous detection as the prior probability of the state detection quantum state for this detection.

[0081] Among them, the preset prior probability refers to the probability distribution set based on historical statistical data during the initialization stage of the system, and is used to initialize the possibility of the state detection quantum state in the first detection.

[0082] Exemplarily, in the case where this detection is the first detection, the terminal calls the preset prior probability of the system as the prior probability of the state detection quantum state for this detection. These probability values are estimated based on historical statistical data and reflect the possibility distribution of the normal state and the attack state. In the case where this detection is not the first detection, the terminal directly calls the posterior probability of the state detection quantum state calculated in the previous detection period and uses it as the prior probability of the current detection.

[0083] In this embodiment, the preset prior probability is used to provide a stable initialization setting for the state detection quantum state during the initial detection, ensuring the reliability of the detection process; while in subsequent detections, by dynamically obtaining the posterior probability of the previous detection period as the prior probability, it can adapt to the changes in the operating state of the power system in real time. Significantly improves the rationality and dynamic adjustment ability of the quantum state initialization, enables the system to have strong robustness and real-time response ability, thus providing efficient support for the subsequent extraction of power system attack information and target state recognition, and improving the detection accuracy of the system in complex scenarios and dynamic environments.

[0084] In an exemplary embodiment, the above step S104 of identifying the target state information of the power system network according to the power system attack information corresponding to the state detection quantum state further includes: determining the target state detection quantum state from the state detection quantum states according to the power system attack information; using the state information corresponding to the target state detection quantum state as the target state information of the power system network.

[0085] Exemplarily, the terminal first analyzes the posterior probability distribution of the state detection quantum states and selects the target quantum state with the largest posterior probability as the target state detection quantum state. If the target quantum state is a normal state quantum state, then the target state information is that the power system network is in a normal state; if the target quantum state is an attack state quantum state of a certain attack type with a certain attack degree, then the target state information is that the power system network is in a state of being attacked by a certain attack type with a certain attack degree.

[0086] In this embodiment, based on the power system attack information, the target state detection quantum state is quickly screened out, and combined with its label and amplitude characteristics, the target state information of the power system is accurately identified. This significantly improves the processing ability for complex attack scenarios. It can not only quickly distinguish the normal state and the attack state, but also refine the types and intensities of attacks. Especially in the composite attack scenario, it can effectively identify the combined characteristics of multiple attacks. Overall, it improves the accuracy, real-time performance, and adaptability of the power system operation state detection, and provides efficient and reliable support for subsequent security protection decisions.

[0087] In an exemplary embodiment, the above step S101 of obtaining the initial quantum state of the power system network further includes: generating a variety of quantum state setting vectors according to the historical power access data of the power system network; the historical power access data includes normal data and attack data; and obtaining the initial quantum state according to the variety of quantum state setting vectors.

[0088] Among them, the historical power access data includes data characteristics such as communication traffic, voltage frequency, load change, power flow, and phase angle of the power system under normal operation and abnormal operation (such as attack state). The quantum state setting vector is a feature vector extracted from the historical power access data and is used to construct the amplitude and phase of the initial quantum state.

[0089] Exemplarily, the terminal extracts access data from the historical database of the power system, and the data includes normal operation data and operation data under different attack states. Then, the terminal performs feature extraction and preprocessing on the historical power access data, including operations such as denoising, normalization, and dimensionality reduction, and generates corresponding quantum state setting vectors from the normal data and the attack data respectively. The terminal uses the quantum state setting vector generated from the normal data to generate the initial quantum state corresponding to the normal state; the terminal uses the quantum state setting vector generated from the attack data to generate the initial quantum state corresponding to the attack state of the corresponding attack type.

[0090] In this embodiment, by combining the historical data of the power system, generating a variety of quantum state setting vectors based on the normal data and the attack data, and constructing the initial quantum state, a high-dimensional and multi-angle characterization of the system's normal state and multiple attack states is achieved, providing a comprehensive basis for subsequent quantum state interference and state detection. Compared with traditional methods, it can extract historical data characteristics more accurately, and enhance the description ability of complex states through quantum state representation, providing higher flexibility and accuracy for the state detection of the power system.

[0091] In an exemplary embodiment, setting vectors according to multiple quantum states to obtain an initial quantum state further includes: generating corresponding multiple basic quantum states according to multiple quantum state setting vectors; generating multiple composite quantum states according to multiple basic quantum states; and integrating multiple basic quantum states and multiple composite quantum states into an initial quantum state.

[0092] Exemplarily, the quantum state setting vector generated by the terminal based on historical power access data generates corresponding basic quantum states through the mapping of amplitude and phase. For example: the quantum state generated by the vector of normal data has a relatively large and stable amplitude, which is used to characterize the characteristics of the normal operation of the system. Then, the terminal uses the generated basic quantum states to generate composite quantum states through quantum entanglement operations, which are used to describe the characteristics of composite states. For example, by linearly combining two or more basic quantum states according to weights, a composite quantum state describing the coexistence of multiple states is generated. Finally, the terminal integrates all the generated basic quantum states and composite quantum states to form a complete set of initial quantum states.

[0093] Specifically, the mapping method of amplitude and phase can be:

[0094] Amplitude mapping: The i-th eigenvalue v i (there are n eigenvalues in total) corresponds to the i-th amplitude component a i of the quantum state, and the mapping formula is:

[0095]

[0096] At the same time, ensure that the amplitude in a quantum state satisfies the normalization condition:

[0097]

[0098] Phase mapping: According to the relative weights or importance of the eigenvalues, map to the phase component φ i of the quantum state, and the mapping formula is: where f() is a preset phase calculation function (such as a linear function or a non-linear transformation).

[0099] Generating quantum states: Combining the amplitude and phase to generate the corresponding basic quantum state Ψ:

[0100]

[0101] where j is the imaginary unit. Specifically, .

[0102] In this embodiment, multiple basic quantum states are generated based on quantum state setting vectors, and composite quantum states are generated through quantum superposition and quantum entanglement operations, and finally integrated into the initial quantum state. This significantly enhances the quantum state characterization ability of the power system operation state. Especially in the composite attack scenario, it can fully capture the joint characteristics and complex correlations of multiple states. Compared with traditional methods, it not only improves the diversity and dynamic adaptability of the initial quantum state, but also provides high-dimensional and high-precision inputs for subsequent quantum state detection and analysis, which helps to improve the accuracy and robustness of power system network state detection.

[0103] In another exemplary embodiment, as Figure 3 shown, the present application provides a power system network attack detection method based on quantum decision information, and the method includes:

[0104] Step S301, data collection and preprocessing.

[0105] Step S302, quantum state initialization.

[0106] Step S303, quantum interference detection.

[0107] Step S304, attack probability calculation.

[0108] Step S305, real-time update and decision-making.

[0109] Step S306, output detection result.

[0110] Exemplarily, in step S301, multi-dimensional network data is collected from the SCADA (Supervisory Control And Data Acquisition) system, smart meters and communication networks of the power system, and the data types include power flow, phase angle, communication traffic, device status, etc. The data is cleaned, dimension-reduced and standardized to generate an input vector suitable for quantum state initialization , where each component represents a specific power parameter feature, such as load current, power change rate or frequency deviation.

[0111] Furthermore, the data preprocessing includes the following sub-steps: (1) Data cleaning: removing noise and outliers in the power system data to ensure the stability of quantum state initialization; (2) Feature dimension reduction: reducing the dimension of high-dimensional data such as power flow, phase angle and frequency through principal component analysis (PCA) or independent component analysis (ICA) to obtain a low-dimensional vector , N' is the number of features after dimension reduction to improve the calculation efficiency; (3) Feature standardization: standardizing the low-dimensional data into a form with a mean of zero and a variance of one to ensure the consistency of the features of the input quantum state and not being affected by the scale differences of different power parameters.

[0112] In step S302, multiple quantum states are initialized based on the preprocessed power system data (where j is the reference to a certain quantum state), and each quantum state represents a specific state of the power system (such as normal operating state, attack state, etc.), including quantum states for mild, moderate, and severe attack states. The amplitude a of the quantum state ij satisfies the normalization condition (where M is the number of amplitudes in a quantum state, corresponding to the feature dimension), which is used to accurately characterize the dynamic changes of the power system. Further, the initialization of the quantum state includes quantum state superposition and entanglement operations to enhance the distinguishability of the power system states, specifically including: (1) Quantum state superposition: Define the normal operating state quantum state (representing the normal state of the power system under stable load conditions) and the attack state quantum state (representing the abnormal state under different attack modes); (2) Quantum state entanglement: For complex attack modes, an entangled state is introduced during the initialization of the quantum state, where α and β represent the weights of the normal state and the attack state respectively, and are used for the detection of composite attack modes.

[0113] Further, for the three common attack types of the power system, namely, Distributed Denial of Service (DDoS) attack, data tampering, and network intrusion, independent quantum states are defined respectively: (1) DDoS attack state: Define the amplitude distribution based on high-frequency traffic characteristics to detect high-load attacks at the network layer; (2) Data tampering attack state: Set the amplitude distribution based on voltage or frequency anomaly analysis to identify data tampering behaviors inside the system; (3) Network intrusion state: Set the amplitude distribution based on abnormal login and data transmission patterns to identify unauthorized intrusion behaviors. The settings of different quantum states enhance the detection accuracy and robustness for various attacks in the power system.

[0114] In step S303, the quantum states are evolved through a quantum interference operation matrix to adjust the amplitudes and phases of the quantum states. The interference matrix U is defined as:

[0115]

[0116] where θ is the interference angle, which is used to distinguish different attack intensities. The evolution process considers the time-varying characteristics of the system state parameters to enhance or suppress specific attack states. Specifically, for different attack intensities (such as mild, moderate, and severe attacks), the interference angle θ can be dynamically adjusted: Mild attack: A smaller interference angle (such as 15°), slightly adjusting the state amplitude to avoid misjudging the normal state; Moderate attack: A medium interference angle (such as 45°), enhancing the attack state amplitude to significantly distinguish it from other states; Severe attack: A larger interference angle (such as 75°), maximizing the attack state amplitude to quickly highlight the severe attack signal.

[0117] For example, assume the attack state is initially represented as:

[0118]

[0119] where α attack represents the initial amplitude of the attack state (the component related to the attack feature); and β attack represents the amplitude of other background states (the component not related to the current attack feature, such as components of other operating states, or natural noise or random fluctuations in the operation of the power system, such as communication noise, small jitters in power load, etc.). After being adjusted by the interference matrix U, the attack state evolves into:

[0120]

[0121] The interference matrix acts uniformly on all attack states (i.e., excluding the normal state), and its effects include: dynamically adjusting the amplitude of the attack state, enhancing the discrimination ability of different attack modes; suppressing the interference of non-attack states, making the features of the attack state more prominent; controlling the detection sensitivity through different interference angles to adapt to various attack intensities. Through this unified processing method, the system can efficiently detect multiple attack modes, improving the detection accuracy and response speed.

[0122] In step S304, calculate the probabilities corresponding to each quantum state , where x t is the current input data collected in real time by the power system. The probability distribution is updated based on the Bayesian inference formula, and the calculation formula is:

[0123]

[0124] where k is the total number of quantum states, including the normal state quantum state and the respective attack state quantum states of different attack degrees, represents the likelihood (i.e., the matching degree) between the current input data and the quantum state , is the prior probability of the quantum state , and the denominator in the formula is the sum of the products of the joint likelihood values and prior probabilities of all quantum states, ensuring that the sum of all posterior probabilities is 1. The calculation results are used to identify the attack state of the current power system.

[0125] In step S305, if this detection is not the first detection, then use the posterior probability of the previous detection as the prior probability of this detection, that is:

[0126]

[0127] where t + 1 corresponds to this detection and t corresponds to the previous detection.

[0128] In a specific example, in the regional power grid of a certain power company, to cope with the risk of cyber attacks, a quantum decision information detection system is deployed to monitor the main attack types as shown in Table 1 below.

[0129] Table 1 Common attack types in the power system

[0130]

[0131] (1) Detection process

[0132] The main steps of the detection system are shown in Table 2 below:

[0133] Table 2 Detection steps

[0134]

[0135] (2) Example application and detection effect

[0136] After the system is implemented, the detection effects of various attack types are as follows:

[0137] 1) Denial-of-service attack detection: When monitoring communication traffic, the system can detect DDoS attacks within 3 seconds under high load (>90% threshold), and the detection accuracy reaches 98%.

[0138] 2) Data tampering attack detection: When monitoring key parameters such as frequency and phase angle, the system has a 99% recognition rate for data anomalies (such as voltage fluctuation >5%), and the response time is within 5 seconds.

[0139] 3) Network intrusion detection: When unauthorized IP addresses or abnormal login behaviors appear in the monitored access logs, the system can identify intrusions within 1 second, and the detection accuracy reaches 97%.

[0140] Table 3 Detection accuracy

[0141]

[0142] Table 4 Detection effect

[0143]

[0144] This detection system effectively enhances the network security of the power system, real-time identifies various attack patterns, and ensures the stability and reliability of the power grid operation.

[0145] The following is a comparison chart of the effects of the power system network attack detection method based on quantum decision information and traditional detection methods (such as rule detection and machine learning methods), covering performance comparisons in aspects such as detection accuracy, response time, adaptability, and noise interference. Through these diagrams, the superiority of this method in enhancing power system network attack detection can be more intuitively demonstrated.

[0146] Figure 4 It shows the accuracy comparison of the quantum decision method, rule-based method, and machine learning method when detecting three types of network attacks (DDoS attack, data tampering, and network intrusion). It can be seen that the quantum decision method has a better detection accuracy than other methods in each attack type. Especially in the detection of data tampering attacks, its accuracy is close to 99%. The rule-based method performs poorly in the detection of DDoS attacks, while the machine learning method has a relatively high accuracy, but it is still lower than the quantum decision method under complex attacks (such as data tampering). This indicates that the quantum decision method can effectively improve the detection accuracy, especially in complex and highly concealed attack types.

[0147] Figure 5 It compares the detection response times of different methods, showing the detection speed performance of each method in the power system environment with high real-time requirements. The average response time of the quantum decision method is 3.2 seconds, significantly faster than the rule-based method (6.4 seconds) and the machine learning method (4.3 seconds). The fast response time enables the quantum decision method to identify and respond to network attacks more promptly, thus better ensuring the security of the power system. This speed advantage is particularly important in dealing with sudden attacks and can help the system take protective measures quickly in the early stage of being attacked.

[0148] Figure 6 It shows the changes in the detection accuracy of each detection method under different noise levels (20%, 40%, 60%). As the noise level increases, the accuracy of the quantum decision method remains relatively stable, and the lowest can still reach 91.2%. In contrast, the accuracies of the rule-based method and the machine learning method decrease significantly under noise interference. Especially, the accuracy of the rule-based method drops to 67.4% at a 60% noise level. This indicates that the quantum decision method has stronger robustness and adaptability in dealing with noise interference and can maintain a relatively high detection accuracy in the complex power system environment.

[0149] Figure 7Shows the adaptability performance of different methods under three attack modes: DDoS attack, data tampering, and network intrusion. The adaptability of the quantum decision method is higher than 95% under all three attack modes, and the adaptability of data tampering detection is the highest, reaching 98.3%. In contrast, the adaptability of the rule-based method is relatively low in DDoS attack detection, while the machine learning method shows a relatively balanced performance under each attack mode but is slightly lower than the quantum decision method. This indicates that the quantum decision method has higher adaptability and generalization ability in dealing with various attack types and can identify different types of network attacks more comprehensively and accurately.

[0150] In this embodiment, through quantum state interference and real-time update, the accuracy, response speed, and noise resistance of power system network attack detection are improved, especially showing better performance in complex and changeable attack scenarios.

[0151] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps does not have a strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0152] Based on the same inventive concept, the embodiments of the present application also provide a power system network attack detection device based on quantum decision information for implementing the above-mentioned power system network attack detection method based on quantum decision information. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the power system network attack detection device based on quantum decision information provided below can refer to the limitations on the power system network attack detection method based on quantum decision information in the above text, and will not be repeated here.

[0153] In an exemplary embodiment, as Figure 8 shown, a power system network attack detection device based on quantum decision information is provided, including: an initial processing module 801, a quantum adjustment module 802, an information determination module 803, and a state determination module 804, where:

[0154] The initial processing module 801 is used to obtain the initial quantum state of the power system network and construct a quantum interference operation matrix of the power system network;

[0155] A quantum adjustment module 802 is configured to adjust an initial quantum state according to a quantum interference operation matrix to obtain an adjusted quantum state as a state detection quantum state. The state detection quantum state includes a normal state quantum state and an attack state quantum state.

[0156] An information determination module 803 is configured to obtain power system attack information corresponding to the state detection quantum state according to the current power access data of the power system network and the state detection quantum state.

[0157] A state determination module 804 is configured to identify target state information of the power system network according to the power system attack information corresponding to the state detection quantum state. The target state information includes normal state information or attack state information.

[0158] In one embodiment, the above-mentioned information determination module 803 is further configured to obtain a likelihood value of the state detection quantum state relative to the current power access data according to the current power access data and the state detection quantum state; obtain a prior probability of the state detection quantum state for this detection; and determine the power system attack information corresponding to the state detection quantum state according to the likelihood value and the prior probability.

[0159] In one embodiment, the above-mentioned information determination module 803 is further configured to obtain a preset prior probability of the state detection quantum state as the prior probability of the state detection quantum state for this detection when this detection is the first detection; and obtain the power system attack information corresponding to the state detection quantum state obtained in the previous detection as the prior probability of the state detection quantum state for this detection when this detection is not the first detection.

[0160] In one embodiment, the above-mentioned state determination module 804 is further configured to determine a target state detection quantum state from the state detection quantum states according to the power system attack information; and use the state information corresponding to the target state detection quantum state as the target state information of the power system network.

[0161] In one embodiment, the above-mentioned initial processing module 801 is further configured to generate a plurality of quantum state setting vectors according to the historical power access data of the power system network. The historical power access data includes normal data and attack data; and obtain an initial quantum state according to the plurality of quantum state setting vectors.

[0162] In one embodiment, the above-mentioned initial processing module 801 is further configured to generate corresponding basic quantum states according to the plurality of quantum state setting vectors; generate a plurality of composite quantum states according to the plurality of basic quantum states; and integrate the plurality of basic quantum states and the plurality of composite quantum states into an initial quantum state.

[0163] Each module in the above-mentioned power system network attack detection device based on quantum decision information can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent thereof, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0164] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structural diagram may be as Figure 9 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a power system network attack detection method based on quantum decision information. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0165] Those skilled in the art can understand that Figure 9 the structure shown in

[0166] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0167] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.

[0168] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.

[0169] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0170] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0171] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0172] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for detecting power system network attacks based on quantum decision information, characterized in that: The method comprises: Acquiring an initial quantum state of a power system network and constructing a quantum interference operation matrix of the power system network; According to the quantum interference operation matrix, the initial quantum state is adjusted to obtain an adjusted quantum state as a state detection quantum state; the state detection quantum state includes a normal state quantum state and an attack state quantum state; According to the current power access data of the power system network and the state detection quantum state, obtaining the power system attack information corresponding to the state detection quantum state; According to the power system attack information corresponding to the state detection quantum state, the target state information of the power system network is identified; the target state information includes normal state information or attack state information.

2. The method according to claim 1, characterized in that The obtaining, according to the current power access data of the power system network and the state detection quantum state, the power system attack information corresponding to the state detection quantum state comprises: Obtaining, according to the current power access data and the state detection quantum state, a likelihood value of the state detection quantum state relative to the current power access data; Obtaining the prior probability of the state detection quantum state for this detection; The power system attack information corresponding to the state detection quantum state is determined according to the likelihood value and the prior probability.

3. The method according to claim 2, characterized in that The obtaining of the prior probability of the state detection quantum state for this detection includes: In the case where the current detection is the first detection, obtaining a preset priori probability of the state detection quantum state as the priori probability of the state detection quantum state for the current detection; In the case that the current detection is not the first detection, the power system attack information corresponding to the state detection quantum state obtained in the previous detection is obtained as the prior probability of the state detection quantum state for the current detection.

4. The method according to claim 1, characterized in that: The step of identifying target state information of the power system network according to the power system attack information corresponding to the state detection quantum state includes: Determining a target state detection quantum state from the state detection quantum state according to the power system attack information; The state information corresponding to the target state detection quantum state is used as the target state information of the power system network.

5. The method according to claim 1, characterized in that The obtaining of the initial quantum state of the power system network comprises: Generate multiple quantum state setting vectors according to historical power access data of the power system network; the historical power access data includes normal data and attack data; The vectors are set according to the multiple quantum states to obtain the initial quantum state.

6. The method according to claim 5, characterized in that The step of setting a vector according to the plurality of quantum states to obtain the initial quantum state comprises: Setting vectors according to the multiple quantum states to generate corresponding multiple basic quantum states; Generating a plurality of composite quantum states according to the plurality of basic quantum states; The multiple basic quantum states and the multiple composite quantum states are integrated into the initial quantum state.

7. A power system network attack detection device based on quantum decision information, characterized in that: The device comprises: An initial processing module, used for obtaining an initial quantum state of the power system network and constructing a quantum interference operation matrix of the power system network; A quantum adjustment module, used to adjust the initial quantum state according to the quantum interference operation matrix to obtain an adjusted quantum state as a state detection quantum state; the state detection quantum state includes a normal state quantum state and an attack state quantum state; An information determination module, configured to obtain power system attack information corresponding to the state detection quantum state according to current power access data of the power system network and the state detection quantum state; The state determination module is used to identify the target state information of the power system network according to the power system attack information corresponding to the state detection quantum state; the target state information includes normal state information or attack state information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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