Side information leakage detection method based on heterogeneous calculation

Abstract

The invention is suitable for the technical field of chip testing, and particularly relates to a side information leakage detection method based on heterogeneous calculation, and the method comprises the steps: recognizing a to-be-detected sample, controlling a data collection device to carry out the data collection of the to-be-detected sample, obtaining the initial side channel information of the to-be-detected sample, and improving the detection pertinence and effectiveness; performing data processing on the initial side channel information of the to-be-tested sample to obtain side channel analysis data of the to-be-tested sample, and ensuring the accuracy and reliability of the data; the method comprises the steps of receiving input information, performing data segmentation on side channel analysis data of a to-be-tested sample according to the input information, determining resource scheduling information of data allocation for performing heterogeneous calculation on the side channel analysis data, and realizing optimization of overall performance; and based on the resource scheduling information of the side channel analysis data, side information leakage detection is performed on the side channel analysis data, and the side information leakage detection result of the to-be-detected sample is determined, so that the detection result is determined quickly, accurately and visually.

Description

Technical Field

[0001] The present application belongs to the field of chip testing technology, and in particular, relates to a side information leakage detection method based on heterogeneous computing. Background Art

[0002] With the rapid development of information technology, data security and privacy protection have become increasingly important issues. In computer systems, side-channel information leakage is a potential security threat, which refers to the attacker inferring sensitive information by observing the indirect output of the system execution process (such as power consumption, electromagnetic radiation, execution time, etc.). This type of leakage is difficult to be directly detected or defended by traditional security protection measures. Therefore, effective side-channel information leakage detection methods are particularly important.

[0003] In the existing technology, side information leakage detection mainly relies on a single processor for data processing and analysis. With the explosive growth of data volume and the complexity of computing tasks, it is difficult to efficiently process large-scale data, resulting in high detection delays and false negative rates. Summary of the invention

[0004] The embodiment of the present application provides a side information leakage detection method based on heterogeneous computing, which can solve the problem in the prior art that it is difficult to efficiently process large-scale data due to the explosive growth of data volume and the complexity of computing tasks, resulting in high detection delays and missed reporting rates.

[0005] In a first aspect, an embodiment of the present application provides a side information leakage detection method based on heterogeneous computing, including: Identify the sample to be tested, and control the data acquisition device to collect data on the sample to be tested, so as to obtain initial side channel information of the sample to be tested; Performing data processing on the initial side channel information of the sample to be tested to obtain side channel analysis data of the sample to be tested; receiving input information, performing data segmentation on the side channel analysis data of the sample to be tested according to the input information, and determining resource scheduling information of the side channel analysis data; wherein the resource scheduling information is used for data allocation for heterogeneous computing of the side channel analysis data; Based on the resource scheduling information of the side channel analysis data, side information leakage detection is performed on the side channel analysis data to determine the side information leakage detection result of the sample to be tested.

[0006] The above technical solutions in the embodiments of the present application have at least the following technical effects: The side information leakage detection method based on heterogeneous computing provided in the embodiment of the present application, by identifying the sample to be tested and controlling the data acquisition device to collect data on the sample to be tested, obtains the initial side channel information of the sample to be tested, and improves the pertinence and effectiveness of the detection. The initial side channel information of the sample to be tested is processed to obtain the side channel analysis data of the sample to be tested, ensuring the accuracy and reliability of the data. Receive input information, perform data segmentation on the side channel analysis data of the sample to be tested based on the input information, determine the resource scheduling information for data allocation for heterogeneous computing of the side channel analysis data, and optimize the overall performance. Based on the resource scheduling information of the side channel analysis data, perform side information leakage detection on the side channel analysis data, determine the side information leakage detection result of the sample to be tested, and achieve fast, accurate and intuitive determination of the detection result.

[0007] In a second aspect, an embodiment of the present application provides a side information leakage detection system based on heterogeneous computing, including: An identification unit, used to identify the sample to be tested, and control the data acquisition device to collect data on the sample to be tested, so as to obtain initial side channel information of the sample to be tested; A processing unit, performing data processing on the initial side channel information of the sample to be tested to obtain side channel analysis data of the sample to be tested; A scheduling unit, for receiving input information, performing data segmentation on the side channel analysis data of the sample to be tested according to the input information, and determining resource scheduling information of the side channel analysis data; wherein the resource scheduling information is used for data allocation for heterogeneous computing of the side channel analysis data; The result unit uses the resource scheduling information based on the side channel analysis data to perform side information leakage detection on the side channel analysis data, and determines the side information leakage detection result of the sample to be tested.

[0008] In a third aspect, an embodiment of the present application provides a side information leakage detection device based on heterogeneous computing, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a method as described in any one of the above aspects when executing the computer program.

[0009] In a fourth aspect, an embodiment of the present application provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute the method of any one of the above aspects.

[0010] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the above aspects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 It is a flow chart of a side information leakage detection method based on heterogeneous computing provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the operation flow of a side information leakage detection method based on heterogeneous computing provided in an embodiment of the present application; Figure 3 This is a step operation display diagram of a side information leakage detection method based on heterogeneous computing provided by an embodiment of the present application; Figure 4 This is a diagram showing the operation of steps in a side information leakage detection method based on heterogeneous computing provided in an embodiment of the present application; Figure 5 This is a diagram showing the operation of steps in a side information leakage detection method based on heterogeneous computing provided in an embodiment of the present application; Figure 6 This is a diagram showing the operation of steps in a side information leakage detection method based on heterogeneous computing provided in an embodiment of the present application; Figure 7 It is a structural diagram of a side information leakage detection system based on heterogeneous computing provided by an embodiment of the present application; Figure 8 It is a structural diagram of a side information leakage detection device based on heterogeneous computing provided in one embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0015] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0016] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0017] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0019] As an emerging computing model, heterogeneous computing optimizes the execution efficiency and performance of computing tasks by combining different types of processors (such as CPU and GPU). CPUs are good at processing logically complex and control-intensive tasks, while GPUs are good at processing large-scale parallel computing tasks. In the field of data security, more efficient and covert side information leakage detection methods can be designed by utilizing the data heterogeneity characteristics between CPUs and GPUs. There are currently many side information leakage detection methods, but most of them mainly rely on a single type of processor (such as using only CPU) for data processing and analysis. These methods have certain limitations in detection efficiency and accuracy. For example, pure software detection methods based on CPUs may have difficulty processing large-scale data in real time due to limited computing resources, resulting in high detection delays and false negative rates. In addition, some studies have attempted to introduce GPUs into side information leakage detection and use the parallel processing capabilities of GPUs to accelerate data processing. However, most of these studies remain in the theoretical exploration stage and fail to fully utilize the data heterogeneity characteristics between CPUs and GPUs to achieve efficient collaborative work.

[0020] In the prior art, due to the lack of a personalized processing mechanism for positioning requests, all devices are allocated the same level of positioning resources and services regardless of their specific needs. This ignores the fact that different IoT terminals have different requirements for positioning accuracy and frequency, resulting in the inability of positioning services to accurately match actual needs and low positioning flexibility.

[0021] To solve the above problems, an embodiment of the present application provides a side information leakage detection method based on heterogeneous computing. In this method, by identifying the sample to be tested and controlling the data acquisition device to collect data on the sample to be tested, the initial side channel information of the sample to be tested is obtained, thereby improving the pertinence and effectiveness of the detection. The initial side channel information of the sample to be tested is processed to obtain the side channel analysis data of the sample to be tested, thereby ensuring the accuracy and reliability of the data. Receive input information, perform data segmentation on the side channel analysis data of the sample to be tested based on the input information, determine the resource scheduling information for data allocation for heterogeneous computing of the side channel analysis data, and optimize the overall performance. Based on the resource scheduling information of the side channel analysis data, perform side information leakage detection on the side channel analysis data, determine the side information leakage detection result of the sample to be tested, and achieve fast, accurate, and intuitive determination of the detection result.

[0022] The side information leakage detection method based on heterogeneous computing provided in the embodiment of the present application can be applied to a terminal device. In this case, the terminal device is the executor of the side information leakage detection method based on heterogeneous computing provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the terminal device.

[0023] For example, the terminal device may be an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a desktop computer, a computer, a laptop computer, a communication device, a computing device, a satellite wireless device, etc.

[0024] In order to better understand the side information leakage detection method based on heterogeneous computing provided in the embodiment of the present application, the specific implementation process of the side information leakage detection method based on heterogeneous computing provided in the embodiment of the present application is exemplarily introduced below.

[0025] Figure 1 A schematic flow chart of a side information leakage detection method based on heterogeneous computing provided in an embodiment of the present application is shown. Figure 2 The operational flow chart of the side information leakage detection method based on heterogeneous computing provided in an embodiment of the present application is shown. The side information leakage detection method based on heterogeneous computing includes: S100, identifying a sample to be tested, controlling a data acquisition device to collect data on the sample to be tested, and obtaining initial side channel information of the sample to be tested.

[0026] It can be understood that the sample to be tested is an actual system or device that needs to be tested for side information leakage, such as an encryption chip, a security processor, an intelligent device, etc. The possible forms of the sample to be tested include IP cores, ASICs, FPGAs, MCUs, etc. While performing its normal functions, the sample to be tested may leak sensitive information through physical side channels (such as power consumption, electromagnetic radiation, current, etc.). The purpose of identifying the sample to be tested is to determine the type, state, and possible side channel characteristics of the sample. Data acquisition can capture side channel information from the sample to be tested. Communication with the data acquisition device can be carried out through the control interface. Communication can use standard protocols such as USB, RS-232 or TCP / IP, and use data acquisition devices (such as power consumption dedicated acquisition devices, current dedicated acquisition devices, electromagnetic probes, etc.) to capture the tiny physical changes generated by the device when performing different operations. The raw data collected by the data acquisition device is the initial side channel information, which is the basis for subsequent analysis and detection.

[0027] Before collecting data on the sample to be tested, the method further includes: S110, receiving sampling configuration information of the sample to be tested; wherein the sampling configuration information is used to determine parameters for data collection of the sample to be tested.

[0028] It can be understood that the sampling configuration information may include a variety of key parameters, such as sampling frequency, sampling duration, signal type, and environmental conditions. Sampling frequency refers to the frequency of data acquisition, usually expressed in Hertz (Hz). It directly affects the resolution of the data. The higher the frequency, the more signal details can be captured. For example, when processing high-frequency signals, a higher sampling frequency needs to be set to avoid aliasing, which may cause data distortion. In addition, the sampling duration also determines the total amount and continuity of the collected data. Too short a duration may result in incomplete data. The determination of the signal type is equally important. Different samples to be tested may involve different types of signals, such as current signals, power signals, or voltage signals. The appropriate signal type can be selected for acquisition based on the characteristics of the sample and the analysis requirements. These configuration information can be received through the human-machine interface (HMI) or programming interface (API).

[0029] S120, setting data acquisition parameters of the sample to be tested based on the sampling configuration information of the sample to be tested.

[0030] It is understood that the sampling configuration information can be applied to the data acquisition settings. The sampling frequency determines the rate of data acquisition, and the sampling frequency information can be applied to the data acquisition settings. The acquisition window can be configured according to the sampling duration. Appropriate signal conditioning devices such as amplifiers and filters can be configured according to the selected signal type to ensure that the signal is maintained within the appropriate range during the acquisition process to avoid overload or signal loss. The set acquisition parameters can be saved and the corresponding configuration files can be generated and called in the subsequent automated data acquisition process to ensure the coherence and consistency of the acquisition process.

[0031] In a possible implementation, S100, identifying a sample to be tested, controlling a data acquisition device and performing data acquisition on the sample to be tested, and obtaining initial side channel information of the sample to be tested, includes: S130, identifying the sample to be tested, controlling the data acquisition device according to the data acquisition parameters of the sample to be tested, and performing data acquisition on the sample to be tested to obtain initial side channel information of the sample to be tested.

[0032] It can be understood that the identification of the sample to be tested involves a variety of methods, such as reading USB descriptors to obtain the manufacturer information and product ID of the device; protocol parsing uses the device's communication protocol (such as USB HID) to understand the device status; the software driver can also interact with the device to achieve the identification of the sample to be tested. In some cases, image recognition technology may also be used to analyze the appearance of the device to automatically identify the device type. After the sample is identified, data acquisition can be performed according to the set data acquisition parameters. The data acquisition module can be called to configure parameters such as sampling frequency, duration, and signal type. Communication with the data acquisition device can be carried out through the control interface. Communication can use standard protocols such as USB, RS-232 or TCP / IP, using data acquisition devices (such as power consumption dedicated acquisition devices, current dedicated acquisition devices, electromagnetic probe pens, etc.) to capture the tiny physical changes generated by the device when performing different operations. The data acquisition device (such as a data acquisition card or oscilloscope) then begins to collect relevant signals (see Figure 3 ), monitor the quality of the collected signals in real time, and the collected signals will be recorded as time series data to form the initial side channel information of the sample to be tested.

[0033] S200, performing data processing on initial side channel information of the sample to be tested to obtain side channel analysis data of the sample to be tested.

[0034] It can be understood that data processing is to extract valuable data features from the initial side channel information to support subsequent analysis. The initial side channel information may be subject to noise and other interference, so effective filtering technology needs to be applied. The selection of filtering algorithms depends on the noise characteristics, and can include Kalman filtering, median filtering, and adaptive filtering. Kalman filtering can dynamically adjust its parameters to adapt to signal changes and is suitable for real-time data processing; median filtering effectively removes impulse noise, while adaptive filtering can automatically adjust its characteristics according to changes in the input signal. After filtering, the data can be statically aligned so that data with different acquisition times can be compared on the same time basis. After static alignment, feature extraction can be performed on the data to extract key features from the processed side channel information. Feature extraction methods can include principal component analysis (PCA) and linear discriminant analysis (LDA). These methods are designed to improve the analyzability and detection efficiency of data, and can effectively reduce dimensionality, mapping high-dimensional data to low-dimensional space, while retaining the main information in the data for subsequent analysis.

[0035] In a possible implementation, S200, performing data processing on initial side channel information of the sample to be tested to obtain side channel analysis data of the sample to be tested, includes: S210 , filtering initial side channel information of the sample to be tested to obtain first side channel analysis data of the sample to be tested.

[0036] It is understood that the initial side channel information of the sample to be tested can be filtered to remove noise and unnecessary signal interference. The purpose of filtering is to improve the quality of the signal and ensure the accuracy of subsequent analysis. The filtering technology may include low-pass filtering, high-pass filtering or band-pass filtering, and the filtering depends on the characteristics of the signal to be tested and the frequency range required for analysis. By filtering the signal, the filtered data is the first side channel analysis data of the sample to be tested, so as to highlight its data characteristics and reduce interference.

[0037] S220, statically aligning the first side channel analysis data of the sample to be tested to obtain second side channel analysis data of the sample to be tested.

[0038] It is understood that static alignment can align the signals of different operations or sample measurements in time (see Figure 4), so that the data can be compared on the same time basis. The starting and ending points of the signal can be determined by analyzing the acquired signal waveform. Feature points, such as the rising or falling edge of the signal, can be identified, which are usually associated with the operation events of the sample to be tested. By detecting these feature points, the starting and ending positions of each signal can be accurately located. Different signals can be translated to align the starting point of each signal to a common time base. Assuming that the sample to be tested generates multiple signals when performing different operations, the starting time of these signals is adjusted to the same moment to achieve temporal consistency. For example, if the starting time of one signal is T1 and the starting time of another signal is T2, the time difference between T1 and T2 can be applied to the signal for translation so that their starting times are the same. After the translation is completed, the signal can be scaled to ensure that the signal is consistent in proportion in the time dimension. This can be achieved by adjusting the sampling rate or interpolation method, especially when the sampling frequencies of different signals are inconsistent. Through interpolation, new data points can be inserted into the signal so that both signals have the same number of data points in the same time period, thereby improving the accuracy of the analysis. After completing the translation and scaling, the generated second side channel analysis data of the sample to be tested will be time-aligned, ensuring the comparability between different measurements, so that effective comparison and judgment can be performed in subsequent analysis.

[0039] S230, obtaining side channel analysis data of the sample to be tested based on the second side channel analysis data of the sample to be tested.

[0040] It can be understood that by conducting an in-depth analysis of the second side channel analysis data, features related to information leakage can be extracted to support subsequent security assessment and detection, and the side channel analysis data of the sample to be tested can be obtained. The side channel analysis data not only contains the information of the original signal, but also incorporates the results of feature extraction and pattern recognition, providing a reliable data basis for subsequent steps. A variety of signal processing techniques can be used, such as time series analysis, frequency domain analysis, etc. By analyzing the statistical characteristics (such as mean, standard deviation, peak, etc.) and frequency domain characteristics (such as frequency spectrum) of the second side channel analysis data, specific patterns or abnormal signals can be identified, which may indicate potential information leakage risks. Such analysis can reveal possible vulnerabilities of the device during operation, thereby providing an important basis for subsequent security detection.

[0041] Optionally, S230, obtaining side channel analysis data of the sample to be tested based on the second side channel analysis data of the sample to be tested, includes: S231, resampling is performed based on the second side channel analysis data of the sample to be tested to obtain third side channel analysis data of the sample to be tested.

[0042] It is understood that the second side channel analysis data of the sample to be tested can be resampled to obtain the third side channel analysis data. The main purpose of resampling is to optimize the sampling rate of the data to meet the needs of subsequent analysis. Resampling can be achieved by upsampling or downsampling. The upsampling process can increase the time resolution by interpolating new data points between the original data points to capture subtle changes in the signal. Downsampling is used to reduce the amount of data, thereby reducing storage requirements and computational burden.

[0043] S232, performing data format conversion on the third side channel analysis data of the sample to be tested to obtain the side channel analysis data of the sample to be tested.

[0044] It can be understood that the purpose of data format conversion of the third side channel analysis data of the sample to be tested is to convert the data into a format suitable for subsequent analysis, so as to facilitate more efficient processing and analysis. Data format conversion can include a variety of operations, such as converting time domain data into frequency domain data, and using algorithms such as fast Fourier transform (FFT) to extract frequency domain features. Data format conversion can also include reorganization of data structures, such as organizing data into a matrix or array form, so as to improve the validity and compatibility of side channel analysis data through data format conversion, thereby making subsequent analysis and detection processes smoother.

[0045] S300, receiving input information, performing data segmentation on the side channel analysis data of the sample to be tested according to the input information, and determining resource scheduling information of the side channel analysis data; wherein the resource scheduling information is used for data allocation for heterogeneous computing of the side channel analysis data.

[0046] It can be understood that relevant parameters and instructions can be received from users or external devices, parsed and stored, and the parsed and stored data is the input information. According to the parameters in the input information, the side channel analysis data can be segmented to define the effective area of ​​the data for subsequent processing. According to the data characteristics and available resources after segmentation, a resource allocation strategy is formulated for the side channel analysis data so that each task can be efficiently executed under reasonable resource configuration, and computing resources and tasks are reasonably allocated according to the requirements of the current task and the hardware resource status of the system (CPU and GPU). In a heterogeneous computing environment, resource allocation is particularly important to ensure that computing tasks are efficiently executed on the most suitable hardware. The resource scheduling process comprehensively considers the real-time nature of the task, the computational complexity and the availability of hardware resources to optimize the overall performance, make full use of the data heterogeneity between the CPU and GPU, achieve efficient collaborative work, and improve the efficiency and accuracy of side information leakage detection.

[0047] In a possible implementation, S300, receiving input information, performing data segmentation on the side channel analysis data of the sample to be tested according to the input information, and determining resource scheduling information of the side channel analysis data, includes: S310, receiving input information, dividing the side channel analysis data of the sample to be tested according to the input information, and determining the starting and ending positions of the curve of the side channel analysis data.

[0048] It is understood that the relevant parameters and instructions received from the user or external device can be parsed to determine the start and end points required for signal analysis. According to the thresholds and conditions parsed and set in the input information, the side channel signal is processed to determine the accurate start and end positions. The start and end positions of the curve refer to the start and end points of the signal in the side channel analysis data, which determines the specific range of data segmentation.

[0049] S320, based on the starting and ending positions of the curve of the side channel analysis data, segment the side channel analysis data of the sample to be tested to obtain a plurality of side channel sub-analysis data.

[0050] It can be understood that the side channel analysis data of the sample to be tested can be cut according to the determined start and end positions to form multiple sub-data sets and obtain multiple side channel sub-analysis data. Each subset contains signal data of a specific time period, which is convenient for subsequent feature extraction and complexity analysis.

[0051] S330, calculating data features for each side channel sub-analysis data, and determining the computational complexity of each side channel sub-analysis data.

[0052] It can be understood that the side channel sub-analysis data is each independent data set obtained from the previous data segmentation process, and the side channel sub-analysis data contains the signal information extracted from the sample to be tested for subsequent analysis. Each side channel sub-analysis data can be subjected to detailed statistical analysis, including calculation of its characteristic values ​​such as mean, variance and standard deviation. The mean reflects the central tendency of the data set, while the variance and standard deviation provide a measure of data volatility, which helps to analyze the stability and degree of change of the signal. Other statistical features, such as maximum, minimum, skewness and kurtosis, can also be calculated to fully understand the distribution characteristics of the data. The computational complexity of each side channel sub-analysis data can be evaluated based on these statistical features, and the time and resources required in the calculation process can be analyzed, especially the complexity of the signal processing algorithm used. For example, the calculation of the mean and standard deviation is usually linear, while more complex statistical feature calculations may require higher computational costs. The computational complexity information of each side channel sub-analysis data can be recorded to provide a basis for subsequent resource scheduling and task allocation, so that the entire analysis process can be carried out efficiently. For example, the process of calculating the mean and standard deviation is usually linear, so its computational complexity can be considered O(n), where n is the number of data points. For more complex statistical feature calculations, such as skewness and kurtosis, more computing resources may be required, and the computational complexity may be nlogn or O(n^2).

[0053] Optionally, S330, calculating a data feature for each side channel sub-analysis data and determining a computational complexity of each side channel sub-analysis data includes: S331, calculating the fractal dimension for each side channel sub-analysis data, and determining the fractal dimension as the computational complexity of each side channel sub-analysis data.

[0054] It can be understood that for each side channel sub-analysis data calculation, the complexity of the signal data can be described by the fractal dimension. The fractal dimension can be calculated by the Box-Counting Method. The data is regarded as a set of points in a dimensional space. Cover these points with boxes of different sizes and record the number of boxes required N(ϵ), where ϵ is the side length of the box. By changing the value of ϵ, a logarithmic graph of N(ϵ) versus ϵ is plotted. The fractal dimension D can be calculated by the following formula: ,The higher the fractal dimension, the greater the complexity of the data, because it represents the degree of detail filled in the data in space. The fractal dimension can be directly used as the computational complexity of the side channel sub-analysis data: C(X)=D. If the fractal dimension of a side channel sub-analysis data is high, its computational complexity is also high because it contains more details and changes.

[0055] S340: Determine resource scheduling information of the side channel analysis data according to the computational complexity of each side channel sub-analysis data.

[0056] It can be understood that the evaluation of computational complexity provides a basis for the effective allocation of resources, ensuring that different computing tasks can be completed efficiently in an appropriate resource environment. Computational complexity reflects the amount of resources required for side-channel sub-analysis data processing, including time complexity and space complexity. Based on the computational complexity, appropriate computing resources can be allocated to each side-channel sub-analysis data. Resource scheduling information can include specifying the type of computing unit to be used (such as GPU or CPU), the amount of memory required, and the pre-calculation of execution time, ensuring that tasks with high complexity can obtain sufficient computing power when resources are limited, thereby avoiding performance bottlenecks caused by insufficient resources.

[0057] Optionally, S340, determining resource scheduling information of the side channel analysis data according to the computational complexity of each side channel sub-analysis data, includes: S341, determining resource requirement information of each side channel sub-analysis data according to the computational complexity of each side channel sub-analysis data.

[0058] It can be understood that resource requirement information refers to a detailed description and quantitative indicators of various resources required to ensure the smooth execution of tasks in computing or data processing tasks. According to the computational complexity of each side channel sub-analysis data, a variety of allocation methods can be used to determine the resource requirement information of each side channel sub-analysis data, aiming to optimize resource usage and improve computing efficiency. It can include static allocation methods, dynamic allocation methods, priority scheduling methods, etc.

[0059] Exemplarily, the static allocation method is a direct resource method. In the static allocation method, fixed resources can be allocated before the task starts according to the estimated computational complexity of each side channel sub-analysis data. Each task will get a certain number of computing cores and memory according to its complexity. For example, if the complexity of a sub-analysis data is high, more GPU cores and memory are allocated to it.

[0060] The dynamic allocation method can monitor the resource usage during the computation in real time and dynamically adjust the resource allocation according to the actual demand. For example, if a side channel sub-analysis data shows a high computational demand during execution, its allocated resources can be automatically increased. Dynamic allocation relies on the implementation of real-time monitoring and scheduling algorithms, and can more effectively respond to changes in computational load.

[0061] Priority scheduling can allocate resources based on the computational complexity of each side channel sub-analysis data and its positively correlated computational priority and importance. High-priority tasks will be given priority to obtain resources, thus ensuring that key analysis can be completed within the specified time. Priority scheduling methods are usually used in conjunction with dynamic allocation to ensure that important tasks can still obtain sufficient computing resources under high load conditions.

[0062] The load balancing method can evenly distribute tasks among multiple computing units by analyzing the load of each computing unit to avoid the situation where one unit is overloaded while another unit is idle. Load balancing can be achieved by using scheduling algorithms (such as polling scheduling, least connection scheduling, etc.) to ensure that the resources of each computing unit are reasonably utilized.

[0063] S342, based on the resource requirement information of each side channel sub-analysis data, allocate a preset heterogeneous computing unit to the resource requirement information of each side channel sub-analysis data to obtain resource matching information of each side channel sub-analysis data; wherein the heterogeneous computing unit includes a GPU.

[0064] It can be understood that heterogeneous computing units refer to different types of processing units, which may include central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), and other dedicated hardware units. Each computing unit has different architectures and performance characteristics and is suitable for specific types of tasks. For example, GPUs perform well when processing a large number of parallel calculations, and are particularly suitable for complex mathematical operations and data parallel processing, while CPUs are suitable for executing control logic and serial computing tasks. According to the resource requirement information of each side channel sub-analysis data, including computing power, memory requirements, storage requirements, and bandwidth requirements, etc. Through the resource requirement information, it is possible to identify which heterogeneous computing units can meet these requirements. For example, if a side channel sub-analysis data requires high computing power and parallel processing capabilities, then the GPU may be allocated first; if a task requires complex control logic, the CPU may be used first. Based on the resource requirement information and the available heterogeneous computing units, each side channel sub-analysis data is allocated to a suitable computing unit, resource matching information for each side channel sub-analysis data is generated, and the specific computing unit and its configuration used for each task are clarified.

[0065] S343, summarizing the resource matching information of all the side channel sub-analysis data, and determining the resource scheduling information of the side channel analysis data.

[0066] It can be understood that resource matching information refers to the computing unit assigned to each side channel sub-analysis data, the required computing resources, and the related configuration parameters. Resource matching information can include not only the type of computing unit used (such as CPU or GPU), but also specific resource allocation, such as memory size, number of processing cores, computing frequency, etc. In the aggregation process, the resource matching information of each side channel sub-analysis data can be integrated, and all individual resource allocations can be collected together to form a comprehensive resource scheduling table, which lists in detail the computing unit allocation, expected computing time, required storage space and other resource requirements of each side channel sub-analysis data. By summarizing the resource matching information, the overall resource usage can be identified to ensure that all computing units are reasonably utilized while avoiding resource conflicts and redundant allocations. For example, if multiple sub-analysis data are assigned to the same GPU, and the computing power of the GPU is limited, it may cause performance bottlenecks. Therefore, the summary information can help adjust the resource allocation strategy, optimize the load of each computing unit, and ensure an efficient computing process.

[0067] S400, based on the resource scheduling information of the side channel analysis data, perform side information leakage detection on the side channel analysis data to determine the side information leakage detection result of the sample to be tested.

[0068] It can be understood that the computing resources can be reasonably configured and utilized through the obtained resource scheduling information to perform efficient side information leakage detection. When performing side information leakage detection, a specific detection algorithm can be applied to identify possible leakage patterns based on the characteristics of each side channel sub-analysis data. It can include correlation analysis, statistical tests, etc. to evaluate whether there are features related to sensitive information in the side channel data. By combining different analysis techniques, a comprehensive assessment of the information that may be leaked during the operation of the sample to be tested is performed. An overall test result is formed by comprehensively analyzing the test results performed by each heterogeneous computing unit. By optimizing the task allocation and data transmission mechanism, the test results of the sample to be tested can be determined quickly, accurately, and intuitively, thereby improving the detection efficiency.

[0069] In a possible implementation, S400, based on the resource scheduling information of the side channel analysis data, performing side information leakage detection on the side channel analysis data, and determining the side information leakage detection result of the sample to be tested, includes: S410, based on the resource scheduling information of the side channel analysis data, each side channel sub-analysis data is allocated to a preset heterogeneous computing unit.

[0070] It can be understood that the resource scheduling information can include a mapping table or decision rule to quickly determine which computing unit each sub-analysis data should be assigned to, ensuring that each sub-analysis data can complete its analysis task in the best computing environment, thereby improving the overall detection performance and response speed.

[0071] S420: Perform side information leakage detection on the allocated side channel sub-analysis data on each preset heterogeneous computing platform to obtain a heterogeneous computing result corresponding to each preset heterogeneous computing unit.

[0072] It can be understood that heterogeneous computing platforms are usually composed of different types of computing units, such as CPU, GPU, FPGA, etc., each with different computing capabilities and characteristics. By utilizing these heterogeneous computing resources, complex data sets can be processed more efficiently. Each side channel sub-analysis data can be sent to the corresponding computing unit, and the calculation can be optimized according to the processing power and characteristics of the unit. Each computing unit will independently execute the side information leakage detection algorithm, and can use statistical analysis, pattern recognition, correlation calculation and other technologies to identify potential side channel leakage. Each computing unit will process data in parallel, significantly improve processing efficiency, and reduce overall computing time. The heterogeneous computing results corresponding to each preset heterogeneous computing unit are output in the form of data. The heterogeneous computing results provide basic data for subsequent aggregation and decision-making, which helps to comprehensively evaluate the safety and potential risks of the samples to be tested.

[0073] Optionally, S420, performing side information leakage detection on the allocated side channel sub-analysis data on each preset heterogeneous computing platform to obtain a heterogeneous computing result corresponding to each preset heterogeneous computing unit, including: S421, performing correlation calculation on the allocated side channel sub-analysis data on each preset heterogeneous computing platform to determine correlation data of the side channel sub-analysis data allocated by each preset heterogeneous computing platform.

[0074] It can be understood that the main purpose of correlation calculation is to analyze the relationship between the side channel sub-analysis data assigned to each preset heterogeneous computing platform. By calculating the correlation (see Figure 5 ), it is possible to determine how different side channel sub-analysis data influence or correlate with each other, thereby identifying potential information leakage paths. Statistical methods, such as the Pearson correlation coefficient and the Spearman rank correlation coefficient, can be used to quantify the degree of correlation between different data sets. When performing correlation calculations, correlation analysis can be performed through statistical software or programming tools (such as Python, R, etc.) to generate a correlation matrix that shows the relationship between different data. High correlation values ​​usually indicate a strong dependency between data, which may indicate potential security vulnerabilities.

[0075] S422, performing a statistical test on the correlation data of the side channel sub-analysis data allocated to each preset heterogeneous computing platform to obtain a heterogeneous computing result corresponding to each preset heterogeneous computing unit.

[0076] It can be understood that the statistical test of the correlation data of the side channel sub-analysis data assigned to each preset heterogeneous computing platform is to verify the significance of the correlation calculation results. Hypothesis testing methods such as t-test, analysis of variance (ANOVA), etc. can be used to determine whether the correlation is statistically significant, thereby ensuring the reliability of the test results. Through statistical testing, it is possible to identify which correlations are accidental and which are of practical significance.

[0077] S430, summarizing the heterogeneous computing results corresponding to each preset heterogeneous computing unit to determine the side information leakage detection result of the sample to be tested.

[0078] It can be understood that the calculation results returned by each heterogeneous computing unit (such as GPU, FPGA, etc.) can be aggregated and centralized, which can include different detection indicators, such as the correlation of side channel data, leakage probability assessment, statistical test results, etc. The heterogeneous computing results corresponding to each preset heterogeneous computing unit are organized in a unified format to ensure that the data between different computing units can be effectively integrated. After the heterogeneous computing results corresponding to the heterogeneous computing units are aggregated, the aggregated results can be comprehensively analyzed to generate the side information leakage detection results of the sample to be tested.

[0079] Optionally, S430, aggregating the heterogeneous computing results corresponding to each preset heterogeneous computing unit to determine the side information leakage detection result of the sample to be tested, including: S431, receiving the heterogeneous computing results corresponding to each preset heterogeneous computing unit, and summarizing all the heterogeneous computing results to obtain the heterogeneous computing summary results of the sample to be tested.

[0080] It can be understood that each preset heterogeneous computing unit can transmit data back to the CPU after determining the corresponding heterogeneous computing results. In the CPU, the calculation results from each heterogeneous computing unit can be received, which may include correlation analysis of side channel data, probability assessment of leakage, etc., and these summary results are generated and stored in a centralized data structure to facilitate subsequent decision support and report generation.

[0081] S432, performing threshold detection on the heterogeneous computing summary results of the sample to be tested, and determining all side information leakage locations corresponding to the heterogeneous computing summary results.

[0082] It is understood that the threshold detection can be set to a predefined threshold (see Figure 6), which is used to determine whether the correlation in the heterogeneous computing summary results exceeds a certain critical point. By comparing each data point in the heterogeneous computing summary results with the threshold, the locations that are considered to be side information leaks are identified. The leaked location may point to a potential source of sensitive information leakage, thus providing important clues for subsequent security analysis.

[0083] S433, determining the side information leakage detection result of the sample to be tested based on all side information leakage locations corresponding to the heterogeneous computing summary results.

[0084] It can be understood that in-depth analysis can be performed based on the leakage locations identified in the heterogeneous computing summary results to determine which locations actually have side information leakage. Based on the analysis results, the side information leakage detection results of the samples to be tested can be classified into different risk levels. These levels can be high, medium, low risk, or specific numerical scores, which help to quickly evaluate the security status of the samples.

[0085] Exemplarily, S433, based on all side information leakage locations corresponding to the heterogeneous computing summary results, determining the side information leakage detection result of the sample to be tested includes: S4331, based on all side information leakage locations corresponding to the heterogeneous computing summary results, determine the side information leakage risk assessment value of the sample to be tested.

[0086] It can be understood that in the process of determining the risk assessment value of side information leakage based on the number of leakage locations, the number of all identified leakage locations can be counted, recorded as N. Then, several thresholds are set to divide the risk level: no leakage location is considered low risk (risk assessment value is 0), A to B leakage locations are medium risk (risk assessment value is 1), and B or more leakage locations are considered high risk (risk assessment value is 2). Through quantitative statistics, the risk assessment of the sample to be tested can be quickly obtained, which is conducive to judging its safety and taking corresponding safety measures.

[0087] S4332, according to the side information leakage risk evaluation value of the sample to be tested, obtain the side information leakage risk detection level of the sample to be tested, and determine the side information leakage risk detection level as the side information leakage detection result of the sample to be tested.

[0088] It can be understood that the side information leakage risk evaluation value that can be determined can be used to judge the risk detection level of the sample to be tested. The risk detection level can be divided into several levels, such as low, medium, high and extremely high. For example, if the risk evaluation value is 0, it may be judged as a low risk; if it is 1, it is a medium risk; if it is 2 or higher, it is a high risk or an extremely high risk. After determining the risk detection level, the side information leakage risk detection level is output together with the test data as the final test result of the sample to be tested to obtain the test result, simplifying the complex test results into risk levels that are easy to understand and apply. By optimizing the task allocation and data transmission mechanism, the test results of the sample to be tested can be determined quickly, accurately and intuitively, thereby improving the detection efficiency.

[0089] Corresponding to the side information leakage detection method based on heterogeneous computing in the above embodiment, the embodiment of the present application also provides a side information leakage detection system based on heterogeneous computing, and each unit of the system can implement each step of the side information leakage detection method based on heterogeneous computing. Figure 7 A structural block diagram of a side information leakage detection system based on heterogeneous computing provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0090] Reference Figure 7 , the side information leakage detection system based on heterogeneous computing includes: An identification unit, used to identify the sample to be tested, and control the data acquisition device to collect data on the sample to be tested, so as to obtain initial side channel information of the sample to be tested; A processing unit, performing data processing on the initial side channel information of the sample to be tested to obtain side channel analysis data of the sample to be tested; A scheduling unit, for receiving input information, performing data segmentation on the side channel analysis data of the sample to be tested according to the input information, and determining resource scheduling information of the side channel analysis data; wherein the resource scheduling information is used for data allocation for heterogeneous computing of the side channel analysis data; The result unit uses the resource scheduling information based on the side channel analysis data to perform side information leakage detection on the side channel analysis data, and determines the side information leakage detection result of the sample to be tested.

[0091] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0092] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0093] The embodiment of the present application also provides a side information leakage detection device based on heterogeneous computing, Figure 8 This is a schematic diagram of the structure of a side information leakage detection device based on heterogeneous computing provided in one embodiment of the present application. Figure 8 As shown, the side information leakage detection device 6 based on heterogeneous computing of this embodiment includes: at least one processor 60 ( Figure 8 Only one is shown), at least one memory 61 ( Figure 8 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the side information leakage detection device 6 based on heterogeneous computing implements the steps in any of the above-mentioned side information leakage detection method embodiments based on heterogeneous computing, or implements the functions of the various units in the above-mentioned system embodiments.

[0094] Exemplarily, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the side information leakage detection based on heterogeneous computing 6.

[0095] The side information leakage detection device 6 based on heterogeneous computing can be a computing device or terminal device such as a desktop computer, a notebook, a PDA, and a cloud server. The side information leakage detection device based on heterogeneous computing can include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 8It is only an example of the side information leakage detection device 6 based on heterogeneous computing and does not constitute a limitation on the side information leakage detection device 6 based on heterogeneous computing. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, buses, etc.

[0096] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0097] In some embodiments, the memory 61 may be an internal storage unit of the side information leakage detection device 6 based on heterogeneous computing, such as a hard disk or memory of the side information leakage detection device 6 based on heterogeneous computing. In other embodiments, the memory 61 may also be an external storage device of the side information leakage detection device 6 based on heterogeneous computing, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the side information leakage detection device 6 based on heterogeneous computing. Further, the memory 61 may also include both the internal storage unit of the side information leakage detection device 6 based on heterogeneous computing and an external storage device. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0098] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0099] An embodiment of the present application provides a computer program product. When the computer program product runs on a side information leakage detection device based on heterogeneous computing, the side information leakage detection device based on heterogeneous computing implements the steps in any of the above method embodiments.

[0100] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the side information leakage detection device based on heterogeneous computing, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0101] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0102] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0103] In the embodiments provided in the present application, it should be understood that the disclosed side information leakage detection system based on heterogeneous computing / side information leakage detection device and method based on heterogeneous computing can be implemented in other ways. For example, the above-described side information leakage detection system based on heterogeneous computing / side information leakage detection device based on heterogeneous computing is merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A side information leakage detection method based on heterogeneous computing, characterized in that: include: Identify the sample to be tested, and control the data acquisition device to collect data on the sample to be tested, so as to obtain initial side channel information of the sample to be tested; Performing data processing on the initial side channel information of the sample to be tested to obtain side channel analysis data of the sample to be tested; receiving input information, performing data segmentation on the side channel analysis data of the sample to be tested according to the input information, and determining resource scheduling information of the side channel analysis data; wherein the resource scheduling information is used for data allocation for heterogeneous computing of the side channel analysis data; Based on the resource scheduling information of the side channel analysis data, side information leakage detection is performed on the side channel analysis data to determine the side information leakage detection result of the sample to be tested.

2. The side information leakage detection method based on heterogeneous computing according to claim 1, characterized in that: Before collecting data on the sample to be tested, the method further includes: Receive sampling configuration information of the sample to be tested; wherein the sampling configuration information is used to determine parameters for data collection of the sample to be tested; Based on the sampling configuration information of the sample to be tested, setting the data acquisition parameters of the sample to be tested; The step of identifying the sample to be tested and controlling a data acquisition device to collect data on the sample to be tested to obtain initial side channel information of the sample to be tested includes: Identify the sample to be tested, and control the data acquisition device to collect data on the sample to be tested according to the data acquisition parameters of the sample to be tested to obtain initial side channel information of the sample to be tested.

3. The side information leakage detection method based on heterogeneous computing according to claim 1, characterized in that: The performing data processing on the initial side channel information of the sample to be tested to obtain the side channel analysis data of the sample to be tested includes: filtering the initial side channel information of the sample to be tested to obtain first side channel analysis data of the sample to be tested; Static alignment is performed on the first side channel analysis data of the sample to be tested to obtain second side channel analysis data of the sample to be tested; Based on the second side channel analysis data of the sample to be tested, the side channel analysis data of the sample to be tested is obtained.

4. The side information leakage detection method based on heterogeneous computing according to claim 3 is characterized in that: The obtaining the side channel analysis data of the sample to be tested based on the second side channel analysis data of the sample to be tested comprises: Resampling based on the second side channel analysis data of the sample to be tested to obtain third side channel analysis data of the sample to be tested; The third side channel analysis data of the sample to be tested is converted into a data format to obtain the side channel analysis data of the sample to be tested.

5. The side information leakage detection method based on heterogeneous computing according to claim 1, characterized in that: The receiving input information, performing data segmentation on the side channel analysis data of the sample to be tested according to the input information, and determining resource scheduling information of the side channel analysis data, comprises: receiving input information, dividing the side channel analysis data of the sample to be tested according to the input information, and determining the starting and ending positions of a curve of the side channel analysis data; Based on the start and end positions of the curve of the side channel analysis data, segmenting the side channel analysis data of the sample to be tested to obtain a plurality of side channel sub-analysis data; Calculating data features for each of the side channel sub-analysis data, and determining the computational complexity of each of the side channel sub-analysis data; Determining resource scheduling information of the side channel analysis data according to the computational complexity of each of the side channel sub-analysis data And / or, calculating data features for each of the side channel sub-analysis data and determining the computational complexity of each of the side channel sub-analysis data comprises: A fractal dimension is calculated for each side channel sub-analysis data, and the fractal dimension is determined as the computational complexity of each side channel sub-analysis data.

6. The side information leakage detection method based on heterogeneous computing according to claim 5, characterized in that: The determining, according to the computational complexity of each of the side channel sub-analysis data, resource scheduling information of the side channel analysis data comprises: Determining resource requirement information of each of the side channel sub-analysis data according to the computational complexity of each of the side channel sub-analysis data; Based on the resource requirement information of each of the side channel sub-analysis data, a preset heterogeneous computing unit is allocated to the resource requirement information of each of the side channel sub-analysis data to obtain resource matching information of each of the side channel sub-analysis data; wherein the heterogeneous computing unit includes a GPU; The resource matching information of all the side channel sub-analysis data is aggregated to determine the resource scheduling information of the side channel analysis data.

7. The side information leakage detection method based on heterogeneous computing according to claim 6 is characterized in that: The performing side information leakage detection on the side channel analysis data based on the resource scheduling information of the side channel analysis data to determine the side information leakage detection result of the sample to be tested includes: Based on the resource scheduling information of the side channel analysis data, each of the side channel sub-analysis data is allocated to a preset heterogeneous computing unit; Performing side information leakage detection on the allocated side channel sub-analysis data on each preset heterogeneous computing platform to obtain a heterogeneous computing result corresponding to each preset heterogeneous computing unit; The heterogeneous computing results corresponding to each preset heterogeneous computing unit are summarized to determine the side information leakage detection result of the sample to be tested.

8. The side information leakage detection method based on heterogeneous computing according to claim 7 is characterized in that: The performing side information leakage detection on the allocated side channel sub-analysis data on each preset heterogeneous computing platform to obtain a heterogeneous computing result corresponding to each preset heterogeneous computing unit includes: Performing correlation calculation on the allocated side channel sub-analysis data on each preset heterogeneous computing platform to determine correlation data of the side channel sub-analysis data allocated by each preset heterogeneous computing platform; A statistical test is performed on the correlation data of the side channel sub-analysis data allocated to each preset heterogeneous computing platform to obtain a heterogeneous computing result corresponding to each preset heterogeneous computing unit.

9. The side information leakage detection method based on heterogeneous computing according to claim 8, characterized in that: The step of aggregating the heterogeneous computing results corresponding to each preset heterogeneous computing unit to determine the side information leakage detection result of the sample to be tested includes: Receiving the heterogeneous computing result corresponding to each preset heterogeneous computing unit, and summarizing all the heterogeneous computing results to obtain the heterogeneous computing summary result of the sample to be tested; Performing threshold detection on the heterogeneous computing summary result of the sample to be tested, and determining all side information leakage locations corresponding to the heterogeneous computing summary result; Based on all the side information leakage locations corresponding to the heterogeneous computing summary results, the side information leakage detection result of the sample to be tested is determined.

10. The side information leakage detection method based on heterogeneous computing according to claim 9, characterized in that: The determining the side information leakage detection result of the sample to be tested based on all the side information leakage positions corresponding to the heterogeneous computing summary result includes: Determine a side information leakage risk assessment value of the sample to be tested based on all side information leakage locations corresponding to the heterogeneous computing summary result; According to the side information leakage risk evaluation value of the sample to be tested, the side information leakage risk detection level of the sample to be tested is obtained, and the side information leakage risk detection level is determined as the side information leakage detection result of the sample to be tested.

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