Side channel attack detection method, apparatus, device, and storage medium

Abstract

This disclosure provides a side-channel attack detection method, apparatus, device, and storage medium, applicable to the field of network security technology. The method includes acquiring waveform information of a component under test, including energy waveform information, electromagnetic waveform information, and time waveform information; extracting features corresponding to the waveform information; invoking a preset correlation analysis mathematical model to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, and the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test; invoking a preset I / O analysis mathematical model to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the component under test; and obtaining the detection result of the component under test based on the calculation results. In this way, it is possible to detect whether a component under test is subject to a side-channel attack, achieving the effects of reducing time consumption, increasing success rate, and improving the efficiency of side-channel attack detection.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more particularly to the field of network security technology, specifically to a side-channel attack detection method, apparatus, device, and storage medium. Background Technology

[0002] The core idea of ​​a side-channel attack (SCA) is to obtain ciphertext information through various leaks generated during the operation of encryption software or hardware. In a narrow sense, a side-channel attack specifically refers to a non-intrusive attack on cryptographic algorithms, breaking the algorithm by leaking side-channel information during the operation of encrypted electronic devices. Narrowly defined side-channel attacks mainly include timing attacks, energy analysis attacks, and electromagnetic analysis attacks targeting cryptographic algorithms. These new types of attacks are far more effective than mathematical methods of cryptanalysis, thus posing a serious threat to cryptographic devices. Currently, the main method used to detect side-channel attacks is energy comparison, which is time-consuming and has a low success rate, failing to adequately reflect side-channel vulnerabilities and resulting in low detection efficiency. Summary of the Invention

[0003] This disclosure provides a side-channel attack detection method, apparatus, device, and storage medium.

[0004] According to a first aspect of this disclosure, a side-channel attack detection method is provided. The method includes:

[0005] Acquire waveform information of the component under test, including energy waveform information, electromagnetic waveform information, and time waveform information;

[0006] Extract the features corresponding to the waveform information;

[0007] A preset correlation analysis mathematical model is invoked to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, as well as the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test; a preset IO analysis mathematical model is invoked to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the component under test.

[0008] The detection results of the component under test are obtained based on the calculation results, and the detection results include whether a side-channel attack exists or not.

[0009] In addition to the aspects and any possible implementations described above, a further implementation is provided in which, after acquiring the waveform information of the component under test, the method further includes:

[0010] Store the waveform information.

[0011] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the extraction of features corresponding to the waveform information includes:

[0012] The waveform information is subjected to autocorrelation processing;

[0013] Based on the waveform information after autocorrelation processing, the features corresponding to the waveform information are extracted.

[0014] As described above and in any possible implementation, a further implementation is provided in which, prior to performing autocorrelation processing on the waveform information, the method further includes:

[0015] The waveform information is preprocessed by elastic alignment, resampling, moving average, group averaging, or waveform calculation.

[0016] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the preset correlation analysis mathematical model is a first-order CPA analysis mathematical model and a plaintext-ciphertext correlation analysis mathematical model.

[0017] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the correlation coefficients of the first-order CPA analysis mathematical model and the plaintext-ciphertext correlation analysis mathematical model include:

[0018]

[0019] Where r represents the correlation coefficient, which is the measure of the degree of linear correlation between the research variables (X, Y); Cov(X, Y) represents the covariance of X and Y; Var[X] represents the variance of X; Var[Y] represents the variance of Y; X represents the feature corresponding to the waveform information; Y represents the key secret information of the component under test.

[0020] According to a second aspect of this disclosure, a side-channel attack detection apparatus is provided. The apparatus includes:

[0021] The acquisition module is used to acquire waveform information of the component under test, including energy waveform information, electromagnetic waveform information and time waveform information;

[0022] The extraction module is used to extract the features corresponding to the waveform information;

[0023] The calculation module is used to call a preset correlation analysis mathematical model to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, as well as the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test; and to call a preset IO analysis mathematical model to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the component under test.

[0024] The generation module is used to obtain the detection results of the component under test based on the calculation results, and the detection results include whether a side-channel attack exists or not.

[0025] According to a third aspect of this disclosure, a side-channel attack detection system is provided. The system includes a secure storage device, an acquisition device, an oscilloscope, and a detection device.

[0026] The secure storage device is used for secure authentication login;

[0027] The acquisition device is used to acquire waveform data of the component under test, including energy waveform data, electromagnetic waveform data and time waveform data.

[0028] The oscilloscope is used to receive the waveform data sent by the acquisition device and convert the waveform data into waveform information, which includes energy waveform information, electromagnetic waveform information and time waveform information.

[0029] The detection device is configured to receive the waveform information sent by the oscilloscope and extract the features corresponding to the waveform information; it is also configured to call a preset correlation analysis mathematical model to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, and the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test; call a preset IO analysis mathematical model to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the component under test; and obtain the detection result of the component under test based on the calculation result, wherein the detection result includes whether a side-channel attack exists or not.

[0030] According to a fourth aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0031] According to a fifth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method described above.

[0032] This application provides a side-channel attack detection method, apparatus, device, and storage medium. It can acquire waveform information of a component under test, including energy waveform information, electromagnetic waveform information, and time waveform information, and extract features corresponding to the waveform information. Then, it calls a preset correlation analysis mathematical model to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, as well as the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test. Next, it calls a preset I / O analysis mathematical model to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the component under test. Based on the above calculation results, it determines whether the component under test has leaked key secret information, obtaining detection results for the component under test, including whether a side-channel attack exists or not. This achieves the effect of detecting whether a component under test is subject to a side-channel attack, reducing time consumption, increasing success rate, and improving the efficiency of side-channel attack detection.

[0033] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0034] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0035] Figure 1 A schematic diagram of an exemplary operating environment in which embodiments of the present disclosure can be implemented is shown;

[0036] Figure 2 It shows Figure 1 The diagram illustrates the interaction method between the secure storage device, acquisition device, oscilloscope, and detection device in the component under test and side-channel attack detection system.

[0037] Figure 3 A schematic diagram of component deployment according to an embodiment of the present disclosure is shown;

[0038] Figure 4 A flowchart of a side-channel attack detection method according to an embodiment of the present disclosure is shown;

[0039] Figure 5 A block diagram of a side-channel attack detection apparatus according to an embodiment of the present disclosure is shown;

[0040] Figure 6 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0042] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0043] In this disclosure, waveform data of energy, electromagnetic, and time leakage information are collected by an acquisition device. An oscilloscope converts the waveform data into waveform information and stores it in a detection device. The detection device calculates the correlation between the features corresponding to the extracted waveform information and the key secret information of the component under test. Based on the calculation results, it determines whether the component under test has leaked key secret information, and obtains the detection results of the component under test, including whether there is a side-channel attack or not. This achieves the effect of detecting whether the component under test has a side-channel attack, reducing time consumption, increasing success rate, and improving the efficiency of side-channel attack detection.

[0044] Figure 1 A schematic diagram of an exemplary operating environment 100 in which embodiments of the present disclosure can be implemented is shown. The operating environment 100 includes a component under test 101 and a side-channel attack detection system 102, the side-channel attack detection system 102 including a secure storage device 1021, an acquisition device 1022, an oscilloscope 1023, and a detection device 1024.

[0045] Figure 2 It shows Figure 1 This diagram illustrates the interaction method between the component under test 101 and the side-channel attack detection system 102, including the secure storage device 1021, the acquisition device 1022, the oscilloscope 1023, and the detection device 1024.

[0046] In frame 201, secure authentication login is completed between secure storage device 1021 and component under test 101.

[0047] In frame 202, the acquisition device 1022 acquires waveform data of the component under test 101, including energy waveform data, electromagnetic waveform data and time waveform data.

[0048] In box 203, the acquisition device 1022 sends waveform data to the oscilloscope 1023.

[0049] In box 204, the oscilloscope 1023 receives waveform data sent by the acquisition device 1022 and converts the waveform data into waveform information, which includes energy waveform information, electromagnetic waveform information and time waveform information.

[0050] In box 205, oscilloscope 1023 sends waveform information to testing device 1024.

[0051] In box 206, the detection device 1024 receives waveform information sent by the oscilloscope 1023 and extracts the features corresponding to the waveform information.

[0052] In box 207, the detection device 1024 calls a preset correlation analysis mathematical model to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, as well as the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test; it also calls a preset IO analysis mathematical model to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the component under test.

[0053] In box 208, the detection device 1024 obtains the detection result of the component under test based on the calculation result, which includes whether a side-channel attack exists or not.

[0054] According to the embodiments of this disclosure, the following technical effects are achieved:

[0055] After secure authentication and login between the secure storage device and the component under test (DUT), the acquisition device collects waveform data from the DUT, including energy waveform data, electromagnetic waveform data, and time waveform data. An oscilloscope receives the waveform data from the acquisition device and converts it into waveform information, including energy waveform information, electromagnetic waveform information, and time waveform information. A detection device receives the waveform information from the oscilloscope and extracts the corresponding features. The detection device uses a preset correlation analysis mathematical model to calculate the correlation between the features corresponding to the energy waveform information and the critical secret information of the DUT, as well as the correlation between the features corresponding to the electromagnetic waveform information and the critical secret information of the DUT. It also uses a preset I / O analysis mathematical model to calculate the correlation between the features corresponding to the time waveform information and the critical secret information of the DUT. Based on the calculation results, the detection device determines whether the DUT has leaked critical secret information, obtaining detection results including whether a side-channel attack exists or not. This achieves the goal of detecting whether a side-channel attack exists in the DUT, reducing time consumption, increasing success rate, and improving the efficiency of side-channel attack detection.

[0056] Figure 3 A schematic diagram of component deployment according to an embodiment of the present disclosure is shown. (As follows) Figure 3 As shown, the components for implementing the side-channel attack detection method include physical layer components, service layer components, and application layer components.

[0057] The physical layer components include secure storage devices, acquisition devices, oscilloscopes, and testing devices.

[0058] In some embodiments, the secure storage device may be a UKey used for secure authentication login with the component under test.

[0059] In some embodiments, the acquisition device may be a multi-functional analyzer and an electromagnetic probe, used to acquire the leakage waveform of the component under test. An amplifier module may be included within the acquisition device to amplify the leakage waveform, so that the amplified leakage waveform can be input to an oscilloscope for subsequent analysis of critical confidential information of the object under test. The detection device may be a host computer, such as a computer.

[0060] In some embodiments, when performing side-channel attack detection on a component under test (DUT), an energy, electromagnetic, or time information acquisition device can be implanted between the detection device and the DUT to acquire waveform information of command execution within the DUT without damaging it. After storing the waveform information using an oscilloscope, leakage information analysis can be performed on the detection device, ultimately achieving the goal of detecting leakage in the DUT.

[0061] The service layer components include autocorrelation, elastic alignment, resampling, moving average, grouped averaging, waveform calculation, first-order intelligent plant algorithm (CPA) analysis, plaintext-ciphertext correlation analysis, and timing I / O analysis.

[0062] The service layer components can provide the application layer with preprocessing services such as autocorrelation, elastic alignment, resampling, moving average, grouped averaging, and waveform calculation. They can also provide mathematical model support for the application layer, such as first-order CPA analysis, plaintext-ciphertext correlation analysis, and timing I / O analysis.

[0063] In some embodiments, a first-order CPA analysis mathematical model can determine the correct value of the true critical secret information by calculating correlation coefficients when the leakage location is known. A plaintext-ciphertext correlation analysis mathematical model can calculate the correlation between plaintext / ciphertext and waveform information, thereby identifying the portion of the waveform information that performs operations related to plaintext / ciphertext, such as plaintext or ciphertext transfer. Generally, the region between plaintext and ciphertext transfer contains the encryption interval; therefore, the encryption interval can be roughly determined based on the plaintext-ciphertext correlation analysis mathematical model. A timing I / O analysis mathematical model can deduce critical secret information by analyzing the execution time of the algorithm run by the component under test. Specifically, each logical operation requires time to execute on a computer; depending on the input, its execution time is precisely measured, and critical secret information is deduced from the execution time.

[0064] The application layer components include energy analysis, electromagnetic analysis, timing analysis, waveform management, waveform acquisition, report generation, and waveform preprocessing components.

[0065] The application layer components can manage and preprocess the acquired waveform information, call mathematical algorithms to test the components under test, and generate evaluation reports.

[0066] In some embodiments, energy analysis can utilize mathematical models such as first-order CPA analysis and plaintext-ciphertext correlation analysis to perform energy leakage analysis and detection on preprocessed energy waveform information. Electromagnetic analysis can utilize mathematical models such as first-order CPA analysis and plaintext-ciphertext correlation analysis to perform electromagnetic leakage analysis and detection on preprocessed electromagnetic waveform information. Timing I / O analysis can utilize mathematical models such as timing I / O analysis to perform timing leakage analysis and detection on preprocessed time waveform information.

[0067] Figure 4 A flowchart of a side-channel attack detection method 400 according to an embodiment of the present disclosure is shown. Method 400 can be derived from... Figure 1 The testing equipment 1024 in the middle is used for execution.

[0068] In box 410, waveform information of the component under test is obtained, including energy waveform information, electromagnetic waveform information and time waveform information.

[0069] In some embodiments, the waveform information of the component under test (DUT) can be determined by acquiring waveform data of energy, electromagnetic, and temporal leakage information from any instruction given to the DUT. The waveform information of the DUT is used for subsequent analysis of leakage information, i.e., critical secret information.

[0070] In some embodiments, the component under test may be a hardware module determined according to the user's actual needs.

[0071] In some embodiments, the waveform information of the component under test is the waveform information of a known module operating function.

[0072] In box 420, extract the features corresponding to the waveform information.

[0073] In some embodiments, the relevant features between the waveform information and the key secret information can be identified by combining the principle of the waveform information of the component under test, and the features can be extracted.

[0074] In box 430, a preset correlation analysis mathematical model is invoked to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, as well as the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test; a preset IO analysis mathematical model is invoked to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the component under test.

[0075] In some embodiments, the preset correlation analysis mathematical model can be a correlation analysis data model invoked according to the user's actual needs. Invoking the preset relevant information analysis mathematical model can calculate the correlation between the features corresponding to the waveform information and the key secret information of the component under test.

[0076] In some embodiments, by invoking a preset I / O analysis mathematical model, key secret information can be derived by analyzing the execution time of the algorithm running by the component under test. Specifically, each logical operation requires time to execute on a computer. Depending on the input, the execution time is precisely measured, and key secret information is deduced from the execution time.

[0077] Based on this, the correlation between the features corresponding to the time waveform information and the key secret information of the component under test can be calculated by calling the preset IO analysis mathematical model.

[0078] In some embodiments, the correlation between the features corresponding to the waveform information and the key secret information of the component under test is determined by calculating the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test, and the correlation between the features corresponding to the time waveform information and the key secret information of the component under test. This correlation is the calculation result.

[0079] In box 440, the detection results of the component under test are obtained based on the calculation results. The detection results include whether a side-channel attack exists or not.

[0080] In some embodiments, the detection result of the component under test is obtained by using a preset correlation threshold and calculation results.

[0081] For example, if the calculated result is greater than or equal to a preset correlation threshold, the detection result is determined to indicate the presence of a side-channel attack. Conversely, if the result is less than or equal to a threshold, the detection result is determined to indicate the absence of a side-channel attack.

[0082] According to the embodiments of this disclosure, the following technical effects are achieved:

[0083] By acquiring waveform information, including energy waveform information, electromagnetic waveform information, and time waveform information, of the component under test (DUT), and extracting the corresponding features of the waveform information; then, calling a preset correlation analysis mathematical model to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the DUT, as well as the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the DUT; then, calling a preset I / O analysis mathematical model to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the DUT; based on the above calculation results, it is determined whether the DUT has leaked key secret information, and the detection results of the DUT, including whether there is a side-channel attack or not, are obtained, thereby realizing the detection of whether the DUT is subject to a side-channel attack, achieving the effect of reducing time consumption, increasing success rate, and improving the efficiency of side-channel attack detection.

[0084] In some embodiments, the waveform information to be analyzed and the test results can also be exported to a file in a preset format to quickly generate a test report. The test report may include test information, test waveforms, and test results for easy viewing and retention by users.

[0085] It should be noted that, based on the correlation between the calculated energy waveform information and the key secret information of the component under test, the correlation between the calculated energy waveform information and the key secret information of the component under test, and the correlation between the calculated time waveform information and the key secret information of the component under test, and the corresponding preset correlation threshold, the detection result of whether the component under test is leaking energy, electromagnetically, or in time can be determined.

[0086] In some embodiments, after obtaining the waveform information of the component under test, the method further includes:

[0087] Store waveform information.

[0088] In some embodiments, waveform files can be managed. The waveform file can be acquired waveform data including energy waveform data, electromagnetic waveform data, and time waveform data. Alternatively, the waveform file can be acquired waveform information including energy waveform information, electromagnetic waveform information, and time waveform information.

[0089] In some embodiments, users can select a specific folder as the folder for storing waveform files according to their actual needs. Waveform files in this folder can be displayed on the page, and they will also be saved in this path by default.

[0090] According to embodiments of this disclosure, storing waveform information facilitates information saving and retrieval, further reducing time consumption and increasing success rate, thereby improving the efficiency of side-channel attack detection.

[0091] In some embodiments, the features corresponding to the extracted waveform information include:

[0092] Perform autocorrelation processing on the waveform information;

[0093] Based on the waveform information after autocorrelation processing, extract the features corresponding to the waveform information.

[0094] In some embodiments, for waveform information of known operating functions of the component under test, based on autocorrelation processing and combined with the production principle of waveform information, it is possible to understand where the sensitive operations of the component under test are executed, so as to identify the features in the waveform information related to the key secret information of the component under test, and then extract the features corresponding to the waveform information based on the waveform information after autocorrelation processing.

[0095] According to embodiments of this disclosure, by extracting features corresponding to the waveform information based on the waveform information after autocorrelation processing, features related to the key secret information of the component under test can be extracted more specifically when it is known where the component under test may generate leaked information. This facilitates subsequent verification of side-channel attacks, further reducing time consumption and increasing the success rate, thereby improving the efficiency of side-channel attack detection.

[0096] In some embodiments, prior to performing autocorrelation processing on the waveform information, the method further includes:

[0097] Perform preprocessing on waveform information, such as flexible alignment, resampling, moving average, group averaging, or waveform calculation.

[0098] In some embodiments, a higher waveform sampling frequency results in more sample data obtained by the detection device per unit time, leading to a more accurate representation of the waveform. However, this also results in excessively large sample data volumes, making it difficult for the detection device to process. Therefore, waveform information can be compressed through resampling preprocessing. Since the set resampling frequency may not be an integer divisor of the sampling frequency, fluctuations within a certain range are allowed. When further processing the waveform information, the set resampling frequency can be used, and the frequency component with the largest fluctuation within the given range can be selected as the final resampling frequency.

[0099] In some embodiments, where nonlinear resampling of waveforms is performed, successful elastic alignment is a prerequisite for statistical analysis of the waveform information. Compared to static alignment, elastic alignment is a dynamic alignment technique. Static alignment aligns the waveform at a specific position by laterally shifting it, while elastic alignment attempts to change every position of the waveform to align them at all positions. Therefore, elastic alignment preprocessing of waveform information can effectively eliminate the effects of random process interruptions and random clock delays, making it more suitable for waveforms that are difficult to align using static alignment, allowing for the use of strategies to stretch and compress the waveform to achieve dynamic alignment.

[0100] In some embodiments, moving average is a signal filtering method similar to low-pass filtering. However, unlike low-pass filtering, moving average achieves its filtering effect by averaging the waveform over a window of size. Therefore, when waveform information contains significant noise, moving average preprocessing can be applied to improve the signal-to-noise ratio.

[0101] In some embodiments, to improve the detection accuracy, the waveform information can be preprocessed by grouping and averaging, that is, the collected waveform information can be divided into several groups and then averaged; the waveform information can also be preprocessed by waveform operation, that is, simple operations such as addition, subtraction, multiplication and division can be performed on two waveforms.

[0102] In some embodiments, preprocessing of waveform information includes, but is not limited to, elastic alignment, resampling, moving average, group averaging, and waveform operations.

[0103] According to embodiments of this disclosure, by performing elastic alignment, resampling, moving average, group averaging, or waveform operation preprocessing on waveform information, it is more conducive to subsequent waveform analysis, thereby reducing time consumption, increasing success rate, and improving the efficiency of side-channel attack detection.

[0104] In some embodiments, the above-mentioned preset correlation analysis mathematical model is a first-order CPA analysis mathematical model and a plaintext-ciphertext correlation analysis mathematical model.

[0105] In some embodiments, plaintext-ciphertext correlation analysis involves calculating the correlation coefficient between the plaintext and ciphertext and waveform information, given the plaintext and ciphertext information, to determine the leakage location. First-order CPA analysis is an attack method for recovering sensitive information. Specifically, it involves guessing key secret information given the leakage location. If encrypting the plaintext using the guessed key secret information results in the same ciphertext, the true key secret information is guessed; otherwise, the guessing continues because the correlation analysis results of the true key secret information will become apparent.

[0106] According to embodiments of this disclosure, by setting the preset correlation analysis mathematical model as a first-order CPA analysis mathematical model and a plaintext-ciphertext correlation analysis mathematical model, side-channel attacks can be detected in both undetermined and known leakage locations, thereby further reducing time consumption, increasing success rate, and improving the efficiency of side-channel attack detection.

[0107] In some embodiments, the correlation coefficients of the above-mentioned first-order CPA analysis mathematical model and the above-mentioned plaintext-ciphertext correlation analysis mathematical model include:

[0108]

[0109] Where r represents the correlation coefficient, which is the measure of the degree of linear correlation between the research variables (X, Y); Cov(X, Y) represents the covariance of X and Y; Var[X] represents the variance of X; Var[Y] represents the variance of Y; X represents the feature corresponding to the waveform information; Y represents the key secret information of the component under test.

[0110] In some embodiments, the correlation coefficient used in the first-order CPA analysis mathematical model and the plaintext-ciphertext correlation analysis mathematical model refers to a value commonly used in statistics to describe the degree of correlation between two variables; it is a statistical indicator used to reflect the closeness of the correlation between variables. Due to the different research objects, the correlation coefficient can be defined in various ways, and it can be a non-deterministic correlation coefficient, such as the Pearson correlation coefficient.

[0111] In some embodiments, the correlation coefficient can be calculated using the product-moment method, which is based on the deviations of the two variables from their respective means. The degree of correlation between the two variables is reflected by multiplying the two deviations. The two variables are the features corresponding to the waveform information and the key secret information of the component under test.

[0112] According to embodiments of this disclosure, the Pearson correlation coefficient can be used as the correlation coefficient between the first-order CPA analysis mathematical model and the plaintext-ciphertext correlation analysis mathematical model to improve the accuracy of determining whether the component under test has leaked critical information.

[0113] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0114] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0115] Figure 5 A block diagram of a side-channel attack detection apparatus 500 according to an embodiment of the present disclosure is shown. The apparatus 500 may be included in... Figure 1 The detection device 1024 is used in or implemented as the detection device 1024. For example... Figure 5 As shown, the device 500 includes:

[0116] The acquisition module 510 is used to acquire waveform information of the component under test, including energy waveform information, electromagnetic waveform information and time waveform information;

[0117] Extraction module 520 is used to extract features corresponding to waveform information;

[0118] The calculation module 530 is used to call a preset correlation analysis mathematical model to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, as well as the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test; and to call a preset IO analysis mathematical model to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the component under test.

[0119] The generation module 540 is used to obtain the detection results of the component under test based on the calculation results. The detection results include whether a side-channel attack exists or not.

[0120] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0121] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0122] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0123] Figure 6 A schematic block diagram of an electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0124] Electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in ROM 602 or a computer program loaded into RAM 603 from storage unit 608. RAM 603 may also store various programs and data required for the operation of electronic device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. I / O interface 605 is also connected to bus 604.

[0125] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of displays, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0126] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as method 400. For example, in some embodiments, method 400 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of method 400 described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform method 400 by any other suitable means (e.g., by means of firmware).

[0127] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0129] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0131] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0132] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0133] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0134] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A side-channel attack detection method, characterized in that, include: Acquire waveform information of the component under test, including energy waveform information, electromagnetic waveform information and time waveform information; Extract the features corresponding to the waveform information; The step of extracting the features corresponding to the waveform information includes: performing autocorrelation processing on the waveform information; and extracting the features corresponding to the waveform information based on the waveform information after autocorrelation processing. A preset correlation analysis mathematical model is invoked to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, as well as the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test; a preset IO analysis mathematical model is invoked to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the component under test; the preset correlation analysis mathematical model is a first-order CPA analysis mathematical model and a plaintext-ciphertext correlation analysis mathematical model; The detection results of the component under test are obtained based on the calculation results, and the detection results include whether a side-channel attack exists or not.

2. The method according to claim 1, characterized in that, After acquiring the waveform information of the component under test, the method further includes: Store the waveform information.

3. The method according to claim 1, characterized in that, Before performing autocorrelation processing on the waveform information, the method further includes: The waveform information is preprocessed by elastic alignment, resampling, moving average, group averaging, or waveform calculation.

4. The method according to claim 1, characterized in that, The correlation coefficients of the first-order CPA analysis mathematical model and the plaintext-ciphertext correlation analysis mathematical model include: Where r represents the correlation coefficient, i.e., the research variable. A measure of the degree of linear correlation between them; This represents the covariance of X and Y; The variance of X is represented by: X represents the variance of Y; X represents the characteristic corresponding to the waveform information; Y represents the key secret information of the component under test.

5. A side-channel attack detection device, characterized in that, include: The acquisition module is used to acquire waveform information of the component under test, including energy waveform information, electromagnetic waveform information and time waveform information; An extraction module is used to extract features corresponding to the waveform information; specifically, the extraction module is used to perform autocorrelation processing on the waveform information; and extract the features corresponding to the waveform information based on the waveform information after autocorrelation processing. The calculation module is used to call a preset correlation analysis mathematical model to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, as well as the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test; and to call a preset IO analysis mathematical model to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the component under test; the preset correlation analysis mathematical model is a first-order CPA analysis mathematical model and a plaintext-ciphertext correlation analysis mathematical model; The generation module is used to obtain the detection results of the component under test based on the calculation results, and the detection results include whether a side-channel attack exists or not.

6. A side-channel attack detection system, characterized in that, This includes secure storage devices, data acquisition devices, oscilloscopes, and testing equipment; The secure storage device is used for secure authentication login; The acquisition device is used to acquire waveform data of the component under test, including energy waveform data, electromagnetic waveform data and time waveform data. The oscilloscope is used to receive the waveform data sent by the acquisition device and convert the waveform data into waveform information, which includes energy waveform information, electromagnetic waveform information and time waveform information. The detection device is used to receive the waveform information sent by the oscilloscope and extract the features corresponding to the waveform information; It is also used to call a preset correlation analysis mathematical model to calculate the correlation between the features corresponding to the energy waveform information and the key secret information of the component under test, as well as the correlation between the features corresponding to the electromagnetic waveform information and the key secret information of the component under test; call a preset IO analysis mathematical model to calculate the correlation between the features corresponding to the time waveform information and the key secret information of the component under test; and obtain the detection result of the component under test based on the calculation result, the detection result including whether a side-channel attack exists or not. The step of extracting the features corresponding to the waveform information includes: performing autocorrelation processing on the waveform information; extracting the features corresponding to the waveform information based on the waveform information after autocorrelation processing; the preset correlation analysis mathematical model is a first-order CPA analysis mathematical model and a plaintext-ciphertext correlation analysis mathematical model.

7. An electronic device, characterized in that, include: At least one processor; as well as A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-4.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.

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