Side channel characteristic analysis system and method based on interlayer correlation evaluation

Abstract

The invention discloses a side channel characteristic analysis system and method based on interlayer correlation evaluation, and relates to the technical field of cryptographic equipment security evaluation and key recovery, and the method comprises the steps: collecting a power consumption curve when cryptographic equipment operates an encryption algorithm, and constructing a power consumption curve matrix; traversing the key search space, and calculating a theoretical intermediate value set of each candidate key in a plurality of continuous operation stages; performing feature correlation analysis on each candidate key, generating a correlation feature curve of each stage, and constructing a layered correlation feature map according to a time sequence; calculating correlation strength between adjacent operation stages according to the atlas to obtain average inter-layer correlation strength; by comparing the average inter-layer correlation strength of the candidate keys, the key with the highest strength is judged as a correct key, and the conflict is resolved by supplementing quantitative indexes.

Description

Technical Field

[0001] This invention relates to the field of cryptographic device security assessment and key recovery technology, specifically a side-channel feature analysis system and method based on inter-layer correlation assessment. Background Technology

[0002] Side-channel analysis, as an important security assessment technique for cryptographic devices, infers and recovers sensitive key data by collecting physical leakage information during the operation of cryptographic chips. Among numerous analysis methods, related analysis techniques, such as correlation power consumption curve analysis, have become the mainstream method due to their high attack efficiency and solid theoretical foundation. The core of this type of method lies in calculating the statistical correlation between the physical leakage curve of the device when processing data and the predicted median value based on key assumptions; the key assumption with the highest correlation is the most likely correct key.

[0003] Although correlation power consumption curve analysis technology is quite mature, its analytical performance in practical high-order or defense scenarios heavily depends on the quality and combination of the selected side-channel features. Existing feature selection methods suffer from two major bottlenecks: 1. Current technology lacks objective standards to evaluate whether a feature region truly contains key-related information. Analysts mostly rely on experience for selection, a blind process that easily misses valid signals or introduces excessive noise. 2. When facing high-order defenses, multiple feature points often need to be combined for attacks. However, existing methods cannot clearly guide which computing nodes' features should be combined, leading to a trial-and-error-based combination strategy, low efficiency, and unstable attack success rate.

[0004] Therefore, there is an urgent need in this field for a novel feature analysis scheme that can fundamentally solve the problems of feature validity quantification and combination node clarification. Summary of the Invention

[0005] The purpose of this invention is to provide a side-channel feature analysis system and method based on inter-layer correlation assessment to solve the problems raised in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a side-channel feature analysis method based on inter-layer correlation assessment, the side-channel feature analysis method comprising: S1. Collect the power consumption curves generated by the cryptographic device when processing known input data while running the encryption algorithm, and form a power consumption curve matrix; S2. Traverse all candidate keys in the key search space. For each candidate key, combine the known input data and the operation logic of the encryption algorithm to calculate the theoretical intermediate value set corresponding to the candidate key in multiple consecutive operation stages of the encryption algorithm. S3. Perform feature correlation analysis for each candidate key: S3.1 For each operation stage, calculate the correlation metric between the power consumption curve matrix and the theoretical intermediate value set of that stage, and generate the correlation characteristic curve for each operation stage. S3.2. Arrange the correlation feature curves of all operation stages in two dimensions according to the execution sequence of the operation stages to construct a hierarchical correlation feature map. The vertical dimension of the map is the time sampling point, and the horizontal dimension is the operation stage layer. S4. Based on the hierarchical correlation feature map, calculate the correlation strength between adjacent operation stage layers corresponding to each candidate key, and obtain the average inter-layer correlation strength of the candidate key. S5. Compare the average inter-layer correlation strength of all candidate keys and determine the candidate key with the highest strength as the correct key; if there are candidate keys with the same strength, eliminate the conflict by supplementing the quantization index and determine the unique correct key.

[0007] Furthermore, the process of constructing the power consumption curve matrix includes: S1.1 Setting acquisition parameters: Sampling rate of the acquisition device ,in, is the key clock frequency of the cryptographic device, k is the sampling rate coefficient; the sampling duration T covers the complete cycle of a single operation of the encryption algorithm, and the number of sampling curves is N; in, , The minimum number of curves required to ensure statistical significance; S1.2 Control the cryptographic device to operate in a shielded environment, for N sets of independent known input data. ,in, For the nth set of input data, collect the corresponding power consumption curves. ,in, This represents the power consumption value at the m-th sampling point of the n-th curve. in, This represents the number of sampling points for a single curve. S1.3 Stack the N power consumption curves row by row to form an N×M dimensional power consumption curve matrix X; ; in, , For the real number field, , ; In the above steps, the power consumption curves include, but are not limited to, power consumption leakage curves, electromagnetic leakage curves, and timing leakage curves; "shielded environment" is, for example, electromagnetic shielding effectiveness ≥60dB, voltage fluctuation ≤±0.05V.

[0008] Furthermore, the calculation process for the theoretical intermediate value set specifically includes: S2.1, Let the continuous operation phase of the encryption algorithm be... ,in, This represents the i-th operation stage, and each operation stage corresponds to a data transformation function. ; S2.2, Let the key search space be... ,in, Let P be the P-th candidate key, where P is the total number of candidate keys; for each candidate key... Perform the following operations: Calculate the theoretical intermediate value: for the nth set of input data Through data transformation function Calculate the theoretical intermediate value of the i-th operation stage ; ; in, Indicates input data With candidate keys After the first Output value after stage operation; Summarize the theoretical intermediate values ​​corresponding to N sets of input data to form the first... The candidate key in the ... The theoretical intermediate value set for the stage: ; Where T denotes matrix transpose. ; In the above steps, the operation phase refers to the operation units in the encryption algorithm that have continuous data dependencies, including but not limited to the round transformation unit (S-box substitution, row shifting, column mixing, round key addition) of block ciphers, the state update unit of stream ciphers, and the modular exponentiation / modular multiplication unit of public-key ciphers.

[0009] Furthermore, the specific process of step S3.1 includes: 3.1.1 Extract the curve vector corresponding to the m-th sampling point from the power consumption curve matrix X. ;in, ; ; 3.1.2 Choosing a correlation measurement function: If Pearson correlation coefficient is used, then the first... The candidate key in the ... The correlation coefficient of the m-th sampling point in the stage is ; ; in, Let covariance function be used. It is a function of standard deviation; ; in, for The mean, for The mean; ; ; If mutual information is used, then the first The candidate key in the ... Mutual information of the m-th sampling point in stage As a measure of correlation; ; in, for The set of possible values, for The set of possible values, For probability density function (for discrete scenarios, for probability mass function); 3.1.3, the first The candidate key in the ... The correlation metrics of all sampling points in a stage are arranged in the sampling order to form the correlation characteristic curve for that stage. ;in, This is the correlation metric for the m-th sampling point; or ; In step S3.2, the hierarchical correlation feature map Given an L×M dimensional matrix, the formula for constructing the hierarchical correlation feature map is: ; in, The i-th row of the matrix corresponds to the first row. The correlation characteristic curve of the stage, with the correlation metric value of the m-th sampling point corresponding to the m-th column; In the above steps, the correlation measures include, but are not limited to, linear correlation measures (Pearson correlation coefficient), rank correlation measures (Spearman correlation coefficient), and information-theoretic correlation measures (mutual information). Furthermore, the specific process of step S4 includes: S4.1, Define the inter-layer similarity function: Let the adjacent operation stage be... and The corresponding correlation characteristic curve is and The possible forms of the inter-layer similarity function include cosine similarity or the inverse of dynamic time-warped distance; The cosine similarity is: ; in, Represents the norm, , ; Among them, the reciprocal of the dynamic time-warped distance: calculate and Dynamic time-warped distance: ; in, The set of all time-warped paths that satisfy monotonicity and continuity. The normalized mapping position of the m-th sampling point; Calculate inter-layer similarity: ; Adding 1 to the denominator avoids infinity when the distance is 0; S4.2 Calculate the correlation strength between individual layers: For each adjacent stage... The inter-layer correlation strength is calculated using a selected similarity function. ; ; S4.3 Calculate the average inter-layer correlation strength: Take the arithmetic mean of the correlation strengths of L-1 adjacent stages to obtain the first... Average interlayer correlation strength of candidate keys ; ; in, (Similarity function output normalization), the larger the value, the more significant the inter-layer correlation of the candidate keys; In the above steps, the correlation strength between adjacent operation stages is calculated through inter-layer similarity functions, which include vector similarity functions (cosine similarity, Pearson correlation coefficient) and distance similarity functions (dynamic time warp distance reciprocal, Euclidean distance reciprocal).

[0010] Furthermore, the specific process of step S5 includes: S5.1 Calculate the average inter-layer correlation strength of all candidate keys. ,right Perform a traversal to find the maximum average inter-layer correlation strength. Screening out those with average inter-layer correlation strength equal to candidate key set ; S5.2 Conflict Resolution: like ,but The candidate key in the data is the correct key. ;in, Indicates the number of elements in the set; like Calculate each Total correlation strength ; ; in, The absolute value reflects the overall correlation level of the spectrum. Perform a traversal to find the maximum total correlation strength Filter out candidate key set ; like ,but The candidate key in is ;like Increase the number of power consumption curves collected. ( (For the curve increment coefficients), repeat steps S1-S5 until... Determine the unique correct key .

[0011] Furthermore, in order to better implement the above method, the present invention provides a side channel feature analysis system based on inter-layer correlation assessment. The side channel feature analysis system includes: a curve acquisition module, an intermediate value calculation module, a feature analysis module, an inter-layer strength assessment module, and a key judgment module. The curve acquisition module is used to acquire the power consumption curve generated by the cryptographic device when processing known input data while running the encryption algorithm, and to construct a power consumption curve matrix; The intermediate value calculation module is used to traverse all candidate keys in the key search space, and combine the known input data with the operation logic of the encryption algorithm to calculate the theoretical intermediate value set corresponding to each candidate key in multiple consecutive operation stages. The feature analysis module is used to perform feature correlation analysis on each candidate key, including calculating the correlation feature curves for each operation stage and constructing a hierarchical correlation feature map. The inter-layer strength evaluation module is used to calculate the correlation strength between adjacent operation stage layers corresponding to each candidate key based on the hierarchical correlation feature map, and obtain the average inter-layer correlation strength. The key determination module is used to compare the average interlayer correlation strength of all candidate keys, determine the candidate key with the highest strength as the correct key, and eliminate conflicts by supplementing quantitative indicators when conflicts exist.

[0012] Furthermore, the curve acquisition module includes: a parameter setting unit, a curve acquisition control unit, and a curve matrix construction unit; The parameter setting unit is used to set the sampling rate, acquisition duration, and number of curves of the acquisition device, ensuring that the sampling rate matches the clock frequency of the cryptographic device and that the acquisition duration covers the complete cycle of a single operation of the encryption algorithm. The curve acquisition and control unit is used to control the operation of cryptographic devices in a shielded environment and to acquire the corresponding power consumption curves for multiple sets of known input data. The curve matrix building unit is used to stack multiple power consumption curves row by row to form a power consumption curve matrix.

[0013] Furthermore, the feature analysis module includes: a correlation measurement unit, a feature curve generation unit, and a hierarchical map construction unit; The correlation measurement unit is used to extract the sampling point curve vector from the power consumption curve matrix and combine it with the theoretical intermediate value set to calculate the correlation measurement value of each sampling point; The feature curve generation unit is used to arrange the correlation metric values ​​of all sampling points in each operation stage according to the sampling order and generate the correlation feature curve for that stage. The hierarchical graph construction unit is used to arrange the correlation feature curves of all stages in two dimensions according to the execution time sequence of the operation stages, and construct a hierarchical correlation feature graph.

[0014] Furthermore, the key determination module includes: a strength comparison unit, a collision detection unit, and a collision resolution unit; The strength comparison unit is used to traverse the average inter-layer correlation strength of all candidate keys, find the maximum strength value, and filter out the set of candidate keys with the highest strength. The conflict detection unit is used to determine whether there are multiple candidate keys with the same maximum average interlayer correlation strength, triggering the conflict resolution mechanism. The conflict resolution unit is used to calculate the total relevance strength of candidate keys when conflicts exist, and further filter for unique candidate keys; if conflicts still exist, the control curve acquisition module increases the number of curves and re-executes the analysis process.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. By constructing a hierarchical correlation feature map, the correlation between features and keys at each operation stage can be objectively evaluated, avoiding the blindness and noise interference caused by the reliance on experience-based screening in traditional methods. 2. By utilizing the inter-layer correlation strength assessment, the combination strategy of features in multiple operation stages is clearly guided, which effectively solves the problem of feature combination relying on trial and error and low efficiency under high-order protection; 3. A dual judgment mechanism of average inter-layer correlation strength and total correlation strength is introduced, combined with a curve increment strategy, which significantly improves the unique key identification capability and system robustness in key conflict scenarios. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the method flow of a side-channel feature analysis system and method based on inter-layer correlation assessment according to the present invention; Figure 2 This is a schematic diagram of the system structure of a side-channel feature analysis system and method based on inter-layer correlation assessment according to the present invention. Detailed Implementation

[0017] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1: As Figure 1 As shown, the present invention provides a technical solution, a side-channel feature analysis method based on inter-layer correlation assessment, the side-channel feature analysis method comprising: S1. Collect the power consumption curves generated by the cryptographic device when processing known input data while running the encryption algorithm, and form a power consumption curve matrix; S2. Traverse all candidate keys in the key search space. For each candidate key, combine the known input data and the operation logic of the encryption algorithm to calculate the theoretical intermediate value set corresponding to the candidate key in multiple consecutive operation stages of the encryption algorithm. S3. Perform feature correlation analysis for each candidate key: S3.1 For each operation stage, calculate the correlation metric between the power consumption curve matrix and the theoretical intermediate value set of that stage, and generate the correlation characteristic curve for each operation stage. S3.2. Arrange the correlation feature curves of all operation stages in two dimensions according to the execution sequence of the operation stages to construct a hierarchical correlation feature map. The vertical dimension of the map is the time sampling point, and the horizontal dimension is the operation stage layer. S4. Based on the hierarchical correlation feature map, calculate the correlation strength between adjacent operation stage layers corresponding to each candidate key, and obtain the average inter-layer correlation strength of the candidate key. S5. Compare the average inter-layer correlation strength of all candidate keys and determine the candidate key with the highest strength as the correct key; if there are candidate keys with the same strength, eliminate the conflict by supplementing the quantization index and determine the unique correct key. The process of constructing the power consumption curve matrix includes: S1.1 Setting acquisition parameters: Sampling rate of the acquisition device ,in, is the key clock frequency of the cryptographic device, k is the sampling rate coefficient; the sampling duration T covers the complete cycle of a single operation of the encryption algorithm, and the number of sampling curves is N; S1.2 Control the cryptographic device to operate in a shielded environment, for N sets of independent known input data. ,in, For the nth set of input data, collect the corresponding power consumption curves. ,in, This represents the power consumption value at the m-th sampling point of the n-th curve. S1.3 Stack the N power consumption curves row by row to form an N×M dimensional power consumption curve matrix X; The calculation process for the theoretical intermediate value set specifically includes: S2.1, Let the continuous operation phase of the encryption algorithm be... ,in, This represents the i-th operation stage, and each operation stage corresponds to a data transformation function. ; S2.2, Let the key search space be... ,in, Let P be the P-th candidate key, where P is the total number of candidate keys; for each candidate key... Perform the following operations: Calculate the theoretical intermediate value: for the nth set of input data Through data transformation function Calculate the theoretical intermediate value of the i-th operation stage ; Summarize the theoretical intermediate values ​​corresponding to N sets of input data to form the first... The candidate key in the ... The theoretical intermediate value set for the stage: ; Where T represents the matrix transpose; The specific process of step S3.1 includes: 3.1.1 Extract the curve vector corresponding to the m-th sampling point from the power consumption curve matrix X. ;in, ; 3.1.2 Choosing a correlation measurement function: If Pearson correlation coefficient is used, then the first... The candidate key in the ... The correlation coefficient of the m-th sampling point in the stage is ; If mutual information is used, then the first The candidate key in the ... Mutual information of the m-th sampling point in stage As a measure of correlation; 3.1.3, the first The candidate key in the ... The correlation metrics of all sampling points in a stage are arranged in the sampling order to form the correlation characteristic curve for that stage. ;in, This is the correlation metric for the m-th sampling point; In step S3.2, the hierarchical correlation feature map Given an L×M dimensional matrix, the formula for constructing the hierarchical correlation feature map is: ; Where the i-th row of the matrix corresponds to the first row. The correlation characteristic curve of the stage, with the correlation metric value of the m-th sampling point corresponding to the m-th column; The specific process of step S4 includes: S4.1, Define the inter-layer similarity function: Let the adjacent operation stage be... and The corresponding correlation characteristic curve is and The possible forms of the inter-layer similarity function include cosine similarity or the inverse of dynamic time-warped distance; S4.2 Calculate the correlation strength between individual layers: For each adjacent stage... The inter-layer correlation strength is calculated using a selected similarity function. ; S4.3 Calculate the average inter-layer correlation strength: Take the arithmetic mean of the correlation strengths of L-1 adjacent stages to obtain the first... Average interlayer correlation strength of candidate keys ; The specific process of step S5 includes: S5.1 Calculate the average inter-layer correlation strength of all candidate keys. ,right Perform a traversal to find the maximum average inter-layer correlation strength. Screening out those with average inter-layer correlation strength equal to candidate key set ; S5.2 Conflict Resolution: like ,but The candidate key in the data is the correct key. ;in, Indicates the number of elements in the set; like Calculate each Total correlation strength ; ; in, For absolute value, Perform a traversal to find the maximum total correlation strength Filter out candidate key set ; like ,but The candidate key in is ;like Increase the number of power consumption curves collected. Repeat steps S1-S5 until... Determine the unique correct key ; Example 2: Figure 2 As shown, in order to better implement the above method, a side channel feature analysis system based on inter-layer correlation assessment is also provided. The side channel feature analysis system includes: a curve acquisition module, an intermediate value calculation module, a feature analysis module, an inter-layer strength assessment module, and a key judgment module. The curve acquisition module is used to acquire the power consumption curve generated by the cryptographic device when processing known input data while running the encryption algorithm, and to construct a power consumption curve matrix. The intermediate value calculation module is used to traverse all candidate keys in the key search space, and combine the known input data with the operation logic of the encryption algorithm to calculate the theoretical intermediate value set corresponding to each candidate key in multiple consecutive operation stages. The feature analysis module is used to perform feature correlation analysis on each candidate key, including calculating the correlation feature curves for each operation stage and constructing a hierarchical correlation feature map. The inter-layer strength evaluation module is used to calculate the correlation strength between adjacent operation stage layers corresponding to each candidate key based on the hierarchical correlation feature map, and obtain the average inter-layer correlation strength. The key judgment module is used to compare the average inter-layer correlation strength of all candidate keys, determine the candidate key with the highest strength as the correct key, and eliminate conflicts by supplementing quantitative indicators when conflicts exist. The curve acquisition module includes: a parameter setting unit, a curve acquisition control unit, and a curve matrix construction unit; The parameter setting unit is used to set the sampling rate, acquisition duration, and number of curves of the acquisition device, ensuring that the sampling rate matches the clock frequency of the cryptographic device and that the acquisition duration covers the complete cycle of a single operation of the encryption algorithm. The curve acquisition and control unit is used to control the operation of cryptographic devices in a shielded environment and to acquire the corresponding power consumption curves for multiple sets of known input data. The curve matrix building unit is used to stack multiple power consumption curves row by row to form a power consumption curve matrix. The feature analysis module includes: a correlation measurement unit, a feature curve generation unit, and a hierarchical map construction unit. The correlation measurement unit is used to extract the sampling point curve vector from the power consumption curve matrix and combine it with the theoretical intermediate value set to calculate the correlation measurement value of each sampling point; The feature curve generation unit is used to arrange the correlation metric values ​​of all sampling points in each operation stage according to the sampling order and generate the correlation feature curve for that stage. The hierarchical graph construction unit is used to arrange the correlation feature curves of all stages in two dimensions according to the execution time sequence of the operation stages, and construct a hierarchical correlation feature graph. The key determination module includes: a strength comparison unit, a collision detection unit, and a collision resolution unit; The strength comparison unit is used to traverse the average inter-layer correlation strength of all candidate keys, find the maximum strength value, and filter out the set of candidate keys with the highest strength. The conflict detection unit is used to determine whether there are multiple candidate keys with the same maximum average interlayer correlation strength, triggering the conflict resolution mechanism. The conflict resolution unit is used to calculate the total relevance strength of candidate keys when conflicts exist, and further filter for unique candidate keys; if conflicts still exist, the control curve acquisition module increases the number of curves and re-executes the analysis process. In an embodiment of the present invention, the AES-128 encryption algorithm is used as an example to illustrate the specific implementation of the present invention; four consecutive stages in the first round of operation are selected as the analysis objects: round key addition, S-box replacement, row shifting, and column mixing; the key search space is set to 256 candidate keys, the sampling rate of the acquisition device is 1GHz, and 5000 power consumption curves are collected; First, the power consumption curve matrix is ​​obtained through the curve acquisition module; the intermediate value calculation module traverses all candidate keys and calculates their theoretical intermediate value sets in the four stages respectively; the feature analysis module uses the Pearson correlation coefficient to generate the correlation feature curve of each stage and constructs a hierarchical correlation feature map in chronological order; the inter-layer strength evaluation module calculates the correlation strength between adjacent stages based on cosine similarity and takes the average to obtain the average inter-layer correlation strength of each candidate key. Finally, the key determination module compares the average strength of all candidate keys and finds that candidate key K_128 has the highest strength and is unique, and determines it to be the correct key.

[0019] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A side-channel feature analysis method based on inter-layer correlation assessment, characterized in that: The side-channel feature analysis method includes: S1. Collect the power consumption curves generated by the cryptographic device when processing known input data while running the encryption algorithm, and form a power consumption curve matrix; S2. Traverse all candidate keys in the key search space. For each candidate key, combine the known input data and the operation logic of the encryption algorithm to calculate the theoretical intermediate value set corresponding to the candidate key in multiple consecutive operation stages of the encryption algorithm. S3. Perform feature correlation analysis for each candidate key: S3.1 For each operation stage, calculate the correlation metric between the power consumption curve matrix and the theoretical intermediate value set of that stage, and generate the correlation characteristic curve for each operation stage. S3.

2. Arrange the correlation feature curves of all operation stages in two dimensions according to the execution sequence of the operation stages to construct a hierarchical correlation feature map. The vertical dimension of the map is the time sampling point, and the horizontal dimension is the operation stage layer. S4. Based on the hierarchical correlation feature map, calculate the correlation strength between adjacent operation stage layers corresponding to each candidate key, and obtain the average inter-layer correlation strength of the candidate key. S5. Compare the average inter-layer correlation strength of all candidate keys and determine the candidate key with the highest strength as the correct key; if there are candidate keys with the same strength, eliminate the conflict by supplementing the quantization index and determine the unique correct key.

2. The side-channel feature analysis method based on inter-layer correlation assessment according to claim 1, characterized in that: The process of constructing the power consumption curve matrix includes: S1.1 Setting acquisition parameters: Sampling rate of the acquisition device ,in, is the key clock frequency of the cryptographic device, k is the sampling rate coefficient; the sampling duration T covers the complete cycle of a single operation of the encryption algorithm, and the number of sampling curves is N; S1.2 Control the cryptographic device to operate in a shielded environment, for N sets of independent known input data. ,in, For the nth set of input data, collect the corresponding power consumption curves. ,in, This represents the power consumption value at the m-th sampling point of the n-th curve. S1.3 Stack the N power consumption curves row by row to form an N×M dimensional power consumption curve matrix X.

3. The side-channel feature analysis method based on inter-layer correlation assessment according to claim 1, characterized in that: The calculation process of the theoretical intermediate value set specifically includes: S2.1, Let the continuous operation phase of the encryption algorithm be... ,in, This represents the i-th operation stage, and each operation stage corresponds to a data transformation function. ; S2.2, Let the key search space be... ,in, Let P be the P-th candidate key, where P is the total number of candidate keys; for each candidate key... Perform the following operations: Calculate the theoretical intermediate value: for the nth set of input data Through data transformation function Calculate the theoretical intermediate value of the i-th operation stage ; Summarize the theoretical intermediate values ​​corresponding to N sets of input data to form the first... The candidate key in the ... The theoretical intermediate value set for the stage: ； Where T represents the matrix transpose.

4. The side-channel feature analysis method based on inter-layer correlation assessment according to claim 1, characterized in that: The specific process of step S3.1 include: 3.1.1 Extract the curve vector corresponding to the m-th sampling point from the power consumption curve matrix X. ;in, ; 3.1.2 Choosing a correlation measurement function: If Pearson correlation coefficient is used, then the first... The candidate key in the ... The correlation coefficient of the m-th sampling point in the stage is ; If mutual information is used, then the first The candidate key in the ... Mutual information of the m-th sampling point in stage As a measure of correlation; 3.1.3, the first The candidate key in the ... The correlation metrics of all sampling points in a stage are arranged in the sampling order to form the correlation characteristic curve for that stage. ;in, This is the correlation metric for the m-th sampling point; In step S3.2, the hierarchical correlation feature map Given an L×M dimensional matrix, the formula for constructing the hierarchical correlation feature map is: ； Where the i-th row of the matrix corresponds to the i-th row of the matrix. The correlation characteristic curve of the stage, with the m-th column corresponding to the correlation metric value of the m-th sampling point.

5. The side-channel feature analysis method based on inter-layer correlation assessment according to claim 1, characterized in that: The specific process of step S4 includes: S4.1, Define the inter-layer similarity function: Let the adjacent operation stage be... and The corresponding correlation characteristic curve is and The possible forms of the inter-layer similarity function include cosine similarity or the inverse of dynamic time-warped distance; S4.2 Calculate the correlation strength between individual layers: For each adjacent stage... The inter-layer correlation strength is calculated using a selected similarity function. ; S4.3 Calculate the average inter-layer correlation strength: Take the arithmetic mean of the correlation strengths of L-1 adjacent stages to obtain the first... Average inter-layer correlation strength of candidate keys .

6. The side-channel feature analysis method based on inter-layer correlation assessment according to claim 1, characterized in that: The specific process of step S5 includes: S5.1 Calculate the average inter-layer correlation strength of all candidate keys. ,right Perform a traversal to find the maximum average inter-layer correlation strength. Screening out those with average inter-layer correlation strength equal to candidate key set ; S5.2 Conflict Resolution: like ,but The candidate key in the data is the correct key. ;in, Indicates the number of elements in the set; like Calculate each Total correlation strength ; ； in, For absolute value, Perform a traversal to find the maximum total correlation strength Filter out candidate key set ; like ,but The candidate key in is ;like Increase the number of power consumption curves collected. Repeat steps S1-S5 until... Determine the unique correct key .

7. A side-channel feature analysis system based on inter-layer correlation assessment, used to execute the side-channel feature analysis method based on inter-layer correlation assessment according to any one of claims 1-6, characterized in that: The side-channel feature analysis system includes: a curve acquisition module, an intermediate value calculation module, a feature analysis module, an inter-layer strength evaluation module, and a key determination module; The curve acquisition module is used to acquire the power consumption curve generated by the cryptographic device when processing known input data while running the encryption algorithm, and to construct a power consumption curve matrix; The intermediate value calculation module is used to traverse all candidate keys in the key search space, and combine the known input data with the operation logic of the encryption algorithm to calculate the theoretical intermediate value set corresponding to each candidate key in multiple consecutive operation stages. The feature analysis module is used to perform feature correlation analysis on each candidate key, including calculating the correlation feature curve of each operation stage and constructing a hierarchical correlation feature map. The inter-layer strength evaluation module is used to calculate the correlation strength between adjacent operation stage layers corresponding to each candidate key based on the hierarchical correlation feature map, and obtain the average inter-layer correlation strength. The key determination module is used to compare the average inter-layer correlation strength of all candidate keys, determine the candidate key with the highest strength as the correct key, and eliminate conflicts by supplementing quantitative indicators when conflicts exist.

8. A side-channel feature analysis system based on inter-layer correlation assessment according to claim 1, characterized in that: The curve acquisition module includes: a parameter setting unit, a curve acquisition control unit, and a curve matrix construction unit; The parameter setting unit is used to set the sampling rate, sampling duration and number of curves of the acquisition device, to ensure that the sampling rate matches the clock frequency of the cryptographic device and the sampling duration covers the complete cycle of a single operation of the encryption algorithm; The curve acquisition and control unit is used to control the cryptographic device to operate in a shielded environment and to acquire the corresponding power consumption curves for multiple sets of known input data. The curve matrix construction unit is used to stack multiple power consumption curves row by row to form a power consumption curve matrix.

9. A side-channel feature analysis system based on inter-layer correlation assessment according to claim 1, characterized in that: The feature analysis module includes: a correlation measurement unit, a feature curve generation unit, and a hierarchical map construction unit; The correlation measurement unit is used to extract the sampling point curve vector from the power consumption curve matrix and, in combination with the theoretical intermediate value set, calculate the correlation measurement value of each sampling point. The feature curve generation unit is used to arrange the correlation measurement values ​​of all sampling points in each operation stage according to the sampling order to generate the correlation feature curve of that stage. The hierarchical graph construction unit is used to arrange the correlation feature curves of all stages in two dimensions according to the execution sequence of the operation stages, and construct a hierarchical correlation feature graph.

10. A side-channel feature analysis system based on inter-layer correlation assessment according to claim 1, characterized in that: The key determination module includes: a strength comparison unit, a conflict detection unit, and a conflict resolution unit; The strength comparison unit is used to traverse the average inter-layer correlation strength of all candidate keys, find the maximum strength value, and filter out the set of candidate keys with the highest strength. The conflict detection unit is used to determine whether there are multiple candidate keys with the same maximum average inter-layer correlation strength, and to trigger the conflict resolution mechanism. The conflict resolution unit is used to calculate the total relevance strength of candidate keys when conflicts exist, and further filter unique candidate keys; if conflicts still exist, the control curve acquisition module increases the number of curves and re-executes the analysis process.

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