Method and device for simulating sniffing attack on screen of mobile equipment

Abstract

The invention discloses a method and device for simulating sniffing attack on a screen of a mobile device, and relates to the technical field of electronic information, and the method comprises the following steps: collecting a to-be-analyzed electromagnetic signal radiated from the surface of the screen of a target device; converting the electromagnetic signal to be analyzed into a corresponding grayscale image; and based on a preset big data model, analyzing the grayscale image corresponding to the to-be-analyzed electromagnetic signal to obtain a corresponding sniffing result. According to the application, the electromagnetic signal radiated by the screen of the mobile equipment is identified, and analysis is carried out based on the preset big data module, so that the corresponding sniffing result is obtained, and a simulation reference basis is provided for information protection work of the mobile equipment.

Description

Technical Field

[0001] The present invention relates to the field of electronic information technology, and in particular to a method and device for simulating sniffing attacks on a mobile device screen. Background Art

[0002] In recent years, mobile devices have gradually played an increasingly important role in people's daily lives due to their portability and rich functionality. Mobile devices such as smartphones and tablets provide a variety of communication methods, and more convenient connections to the Internet also bring new information security risks. As a network attack technology, channel analysis attack technology has been proven to be feasible. Attackers capture sensitive information such as user names and passwords by detecting electromagnetic radiation on the surface of the screen. When attackers are able to sniff the screen content, they may obtain the user's real-time operations and visual information, which may cause privacy leakage and property loss to the user. Therefore, a technology that simulates sniffing attacks is needed to provide a reference for information protection on mobile devices.

[0003] Therefore, in order to meet actual needs, a technology for simulating sniffing attacks on mobile device screens is now provided. Summary of the invention

[0004] In view of the defects existing in the prior art, the purpose of the present invention is to provide a method and device for simulating sniffing attacks on the screen of a mobile device, identifying the electromagnetic signals radiated from the screen of the mobile device, and analyzing them based on a preset big data module to obtain corresponding sniffing results, thereby providing a simulation reference basis for the information protection work of the mobile device.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] In a first aspect, the present application provides a method for simulating a sniffing attack on a mobile device screen, the method comprising the following steps:

[0007] Collect the electromagnetic signals to be analyzed radiated from the screen surface of the target device;

[0008] Converting the electromagnetic signal to be analyzed into a corresponding grayscale image;

[0009] Based on the preset big data model, the grayscale image corresponding to the electromagnetic signal to be analyzed is analyzed to obtain the corresponding sniffing result.

[0010] On the basis of the above technical solution, the method further includes a big data model training process, and the big data model training process includes the following steps:

[0011] Controlling the reference device to execute preset operation instructions to collect reference electromagnetic signals to be analyzed radiated from the screen surface of the reference device;

[0012] Converting the reference electromagnetic signal to be analyzed into a corresponding grayscale image;

[0013] Constructing a model training set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction;

[0014] Based on the model training set, the big data model is trained and obtained.

[0015] On the basis of the above technical solution, a model training set is constructed based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction, including the following steps:

[0016] Segmenting the grayscale image corresponding to the reference electromagnetic signal to be analyzed to obtain a corresponding segmented grayscale image;

[0017] Performing a convolution operation based on the segmented grayscale images corresponding to each of the grayscale images to obtain a convolution result corresponding to each of the grayscale images;

[0018] Based on the segmented grayscale images corresponding to each of the grayscale images, a pooling operation is performed to obtain a pooling result corresponding to each of the grayscale images;

[0019] A model training set is constructed based on the convolution result, the pooling result and the preset operation instruction corresponding to the reference electromagnetic signal to be analyzed.

[0020] On the basis of the above technical solution, the method further comprises the following steps:

[0021] Optimizing the big data model to obtain an optimized big data model;

[0022] Based on the optimized big data model, the grayscale image corresponding to the electromagnetic signal to be analyzed is analyzed to obtain the corresponding sniffing result.

[0023] Based on the above technical solution, the big data model is optimized to obtain an optimized big data model, including the following steps:

[0024] Controlling the reference device to execute preset operation instructions to collect reference electromagnetic signals to be analyzed radiated from the screen surface of the reference device;

[0025] Converting the reference electromagnetic signal to be analyzed into a corresponding grayscale image;

[0026] Constructing a model optimization set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction;

[0027] Based on the optimization of the big data model, the optimized big data model is obtained.

[0028] In a second aspect, the present application further provides a device for simulating sniffing attacks on a mobile device screen, the device comprising:

[0029] A signal acquisition module, which is used to collect electromagnetic signals to be analyzed radiated from the screen surface of the target device;

[0030] An image conversion module, which is used to convert the electromagnetic signal to be analyzed into a corresponding grayscale image;

[0031] The sniffing analysis module is used to analyze the grayscale image corresponding to the electromagnetic signal to be analyzed based on a preset big data model to obtain the corresponding sniffing result.

[0032] On the basis of the above technical solution, the device further comprises a model training module;

[0033] The signal acquisition module is also used to control the reference device to execute a preset operation instruction to acquire a reference electromagnetic signal to be analyzed radiated from the screen surface of the reference device;

[0034] The image conversion module is also used to convert the reference electromagnetic signal to be analyzed into a corresponding grayscale image;

[0035] The model training module is used to construct a model training set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction;

[0036] The model training module is also used to train and obtain the big data model based on the model training set.

[0037] On the basis of the above technical solution, the device further includes a convolution analysis module and a pooling analysis module;

[0038] The image conversion module is also used to segment the grayscale image corresponding to the reference electromagnetic signal to be analyzed to obtain a corresponding segmented grayscale image;

[0039] The convolution analysis module is used to perform a convolution operation based on the segmented grayscale image corresponding to each of the grayscale images to obtain a convolution result corresponding to each of the grayscale images;

[0040] The pooling analysis module is used to perform a pooling operation based on the segmented grayscale images corresponding to each of the grayscale images to obtain a pooling result corresponding to each of the grayscale images;

[0041] The model training module is also used to construct a model training set based on the convolution result, the pooling result and the preset operation instruction corresponding to the reference electromagnetic signal to be analyzed.

[0042] On the basis of the above technical solution, the device further comprises a model optimization module;

[0043] The model optimization module is used to optimize the big data model to obtain an optimized big data model;

[0044] The sniffing analysis module is also used to analyze the grayscale image corresponding to the electromagnetic signal to be analyzed based on the optimized big data model to obtain the corresponding sniffing result.

[0045] On the basis of the above technical solution, the signal acquisition module is further used to control the reference device to execute a preset operation instruction to acquire the reference electromagnetic signal to be analyzed radiated from the screen surface of the reference device;

[0046] The image conversion module is also used to convert the reference electromagnetic signal to be analyzed into a corresponding grayscale image;

[0047] The model optimization module is also used to construct a model optimization set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction;

[0048] The model optimization module is also used to optimize the big data model based on the optimization to obtain the optimized big data model.

[0049] Compared with the prior art, the advantages of the present invention are:

[0050] The present invention identifies the electromagnetic signals radiated from the screen of the mobile device, and analyzes them based on a preset big data module to obtain corresponding sniffing results, thereby providing a simulation reference basis for the information protection work of the mobile device. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments 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 creative work.

[0052] Figure 1 A flowchart of the steps of a method for simulating sniffing attacks on a mobile device screen according to an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of the training process of a big data model in a method for simulating sniffing attacks on a mobile device screen according to an embodiment of the present invention;

[0054] Figure 3 It is a schematic diagram of the operation flow of segmenting an image in the method for simulating sniffing attack on the screen of a mobile device according to an embodiment of the present invention;

[0055] Figure 4 It is a schematic diagram of the operation flow of the convolution operation in the method for simulating sniffing attack on the screen of a mobile device according to an embodiment of the present invention;

[0056] Figure 5 It is a schematic diagram of the operation flow of the pooling operation in the method for simulating sniffing attack on the screen of a mobile device according to an embodiment of the present invention;

[0057] Figure 6 It is a schematic diagram of the operation flow of the classification operation in the method for simulating sniffing attack on the screen of a mobile device according to an embodiment of the present invention;

[0058] Figure 7 A technical schematic diagram of a radio detection system in a method for simulating sniffing attacks on a mobile device screen according to an embodiment of the present invention;

[0059] Figure 8 A technical schematic diagram of a method for simulating sniffing attacks on a mobile device screen according to an embodiment of the present invention;

[0060] Fig. 9 A schematic diagram of the operation flow of a method for simulating sniffing attacks on a mobile device screen according to an embodiment of the present invention;

[0061] Fig.10 The present invention is a structural block diagram of an apparatus for simulating sniffing attacks on a mobile device screen according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0063] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0064] The embodiments of the present application provide a method and device for simulating a sniffing attack on a mobile device screen, identifying electromagnetic signals radiated from the mobile device screen, and analyzing them based on a preset big data module to obtain corresponding sniffing results, thereby providing a simulation reference basis for information protection work on the mobile device.

[0065] In order to achieve the above technical effects, the overall idea of ​​this application is as follows:

[0066] A method for simulating sniffing attack on a mobile device screen, the method comprising the following steps:

[0067] S1. Collecting electromagnetic signals to be analyzed radiated from the screen surface of the target device;

[0068] S2, converting the electromagnetic signal to be analyzed into a corresponding grayscale image;

[0069] S3. Based on the preset big data model, the grayscale image corresponding to the electromagnetic signal to be analyzed is analyzed to obtain the corresponding sniffing result.

[0070] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0071] First, see Figures 1 to 9 As shown, the embodiment of the present application provides a method for simulating sniffing attack on the screen of a mobile device, the method comprising the following steps:

[0072] S1. Collecting electromagnetic signals to be analyzed radiated from the screen surface of the target device;

[0073] S2, converting the electromagnetic signal to be analyzed into a corresponding grayscale image;

[0074] S3. Based on the preset big data model, the grayscale image corresponding to the electromagnetic signal to be analyzed is analyzed to obtain the corresponding sniffing result.

[0075] It should be noted that the core process of the technical solution of the embodiment of the present application includes:

[0076] Use a profile device that is sufficiently similar to the target device (such as a mobile phone, tablet computer, or other device that communicates with the outside world through wireless media and has a screen display function) to collect training data for machine learning;

[0077] Uses antennas and software-defined radios to detect the electromagnetic signals that the device sends to the screen for display.

[0078] In the embodiment of the present application, the electromagnetic signal radiated from the screen of the mobile device is identified and analyzed based on a preset big data module to obtain the corresponding sniffing result, thereby providing a simulation reference basis for the information protection work of the mobile device.

[0079] Furthermore, the method also includes a big data model training process, and the big data model training process includes the following steps:

[0080] Controlling the reference device to execute preset operation instructions to collect reference electromagnetic signals to be analyzed radiated from the screen surface of the reference device;

[0081] Converting the reference electromagnetic signal to be analyzed into a corresponding grayscale image;

[0082] Constructing a model training set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction;

[0083] Based on the model training set, the big data model is trained and obtained.

[0084] Furthermore, based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction, a model training set is constructed, including the following steps:

[0085] Segmenting the grayscale image corresponding to the reference electromagnetic signal to be analyzed to obtain a corresponding segmented grayscale image;

[0086] Performing a convolution operation based on the segmented grayscale images corresponding to each of the grayscale images to obtain a convolution result corresponding to each of the grayscale images;

[0087] Based on the segmented grayscale images corresponding to each of the grayscale images, a pooling operation is performed to obtain a pooling result corresponding to each of the grayscale images;

[0088] A model training set is constructed based on the convolution result, the pooling result and the preset operation instruction corresponding to the reference electromagnetic signal to be analyzed.

[0089] Furthermore, the method further comprises the following steps:

[0090] Optimizing the big data model to obtain an optimized big data model;

[0091] Based on the optimized big data model, the grayscale image corresponding to the electromagnetic signal to be analyzed is analyzed to obtain the corresponding sniffing result.

[0092] Further, optimizing the big data model to obtain an optimized big data model includes the following steps:

[0093] Controlling the reference device to execute preset operation instructions to collect reference electromagnetic signals to be analyzed radiated from the screen surface of the reference device;

[0094] Converting the reference electromagnetic signal to be analyzed into a corresponding grayscale image;

[0095] Constructing a model optimization set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction;

[0096] Based on the optimization of the big data model, the optimized big data model is obtained.

[0097] Based on the technical solution of the embodiment of the present application, the big data model training process is implemented as follows:

[0098] The first step is to segment the image:

[0099] A grayscale image is a bunch of numbers arranged in order in a computer, with values ​​ranging from 0 to 255;

[0100] For black and white images, the values ​​here correspond to different grayscales. For RGB images, the values ​​here represent different proportions of the three primary colors. Before image processing, it is usually necessary to segment the image.

[0101] The second step is convolution operation:

[0102] The convolution layer is the core part of the convolutional neural network, which extracts image features by performing convolution operations on the input image.

[0103] In convolutional neural networks, the convolution operation refers to the operation of multiplying a small movable window with the image element by element and then adding them. This small window is actually a set of fixed weights, which can be regarded as a specific filter or convolution kernel. The name of this operation, "convolution", comes from this element-level multiplication and summation process.

[0104] The third step is pooling operation:

[0105] The pooling layer is used to reduce the spatial resolution of the feature map and reduce the computing pressure;

[0106] The embodiment of the present application takes average pooling as an example and takes the average value in a sliding window as the output.

[0107] Step 4: Classification operation:

[0108] The fully connected layer is usually used to classify or regress the features extracted by the convolutional layer and the pooling layer;

[0109] Its input is a one-dimensional vector, and its output dimension is the same as the dimension of the classification number or regression value of the task. The embodiment of the present application mainly uses some pictures containing numbers for training.

[0110] Furthermore, the embodiment of the present application is configured with a radio detection system, and the screen sniffing attack uses an antenna and a software-defined radio to detect electromagnetic signals sent by the device to the screen for display, such as a message with a verification code. This special device can recreate the signal as a grayscale image, called an emage. It is usually impossible for human observers to directly interpret the image, which can be solved by machine learning (especially deep learning classifiers).

[0111] The collection of electromagnetic signals should be done in places far away from complex electromagnetic environments such as substations to reduce interference in the environment. After amplifying the electromagnetic signal, the signal needs to be sampled, quantized, and encoded to form a digital signal; the preset function of the SDA device can convert this digital signal into an electrical signal, and then convert the electrical signal into an emage image; the emage image is input into the big data model for processing, and the model will give the possibility that each grayscale image corresponds to different numbers.

[0112] In summary, sniffing attacks, also known as electromagnetic side channel attacks, are an attack method that uses electromagnetic radiation generated by a device during operation to obtain information. These radiations may leak information about the internal state of the device, which the attacker can use for analysis and inference.

[0113] In legitimate security research and testing, the benefits of electromagnetic side-channel attacks may include:

[0114] Security Assessment: By analyzing electromagnetic radiation, security researchers can assess a device’s ability to resist side-channel attacks.

[0115] Vulnerability Discovery: It can help researchers discover potential vulnerabilities in hardware and software so that they can take steps to fix them.

[0116] Security of encryption algorithms: During the development of encryption algorithms, electromagnetic side-channel attacks can be used as a testing method to ensure the security of the algorithms.

[0117] Hardware design improvements: Understanding the principles of electromagnetic side-channel attacks can help hardware designers take measures to reduce the risk of information leakage through electromagnetic radiation.

[0118] Security Standard Development: By studying electromagnetic side-channel attacks, stricter security standards and guidelines can be developed to protect user data security.

[0119] Education and training: Understanding these types of attacks can help security experts and technicians better understand the complexities of network security and improve their professional skills.

[0120] Second, see Fig.10 As shown, an embodiment of the present application provides a device for simulating sniffing attack on a mobile device screen, the device comprising:

[0121] A signal acquisition module, which is used to collect electromagnetic signals to be analyzed radiated from the screen surface of the target device;

[0122] An image conversion module, which is used to convert the electromagnetic signal to be analyzed into a corresponding grayscale image;

[0123] The sniffing analysis module is used to analyze the grayscale image corresponding to the electromagnetic signal to be analyzed based on a preset big data model to obtain the corresponding sniffing result.

[0124] In the embodiment of the present application, the electromagnetic signal radiated from the screen of the mobile device is identified and analyzed based on a preset big data module to obtain the corresponding sniffing result, thereby providing a simulation reference basis for the information protection work of the mobile device.

[0125] Furthermore, the device also includes a model training module;

[0126] The signal acquisition module is also used to control the reference device to execute a preset operation instruction to acquire a reference electromagnetic signal to be analyzed radiated from the screen surface of the reference device;

[0127] The image conversion module is also used to convert the reference electromagnetic signal to be analyzed into a corresponding grayscale image;

[0128] The model training module is used to construct a model training set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction;

[0129] The model training module is also used to train and obtain the big data model based on the model training set.

[0130] Furthermore, the device also includes a convolution analysis module and a pooling analysis module;

[0131] The image conversion module is also used to segment the grayscale image corresponding to the reference electromagnetic signal to be analyzed to obtain a corresponding segmented grayscale image;

[0132] The convolution analysis module is used to perform a convolution operation based on the segmented grayscale image corresponding to each of the grayscale images to obtain a convolution result corresponding to each of the grayscale images;

[0133] The pooling analysis module is used to perform a pooling operation based on the segmented grayscale images corresponding to each of the grayscale images to obtain a pooling result corresponding to each of the grayscale images;

[0134] The model training module is also used to construct a model training set based on the convolution result, the pooling result and the preset operation instruction corresponding to the reference electromagnetic signal to be analyzed.

[0135] Furthermore, the device also includes a model optimization module;

[0136] The model optimization module is used to optimize the big data model to obtain an optimized big data model;

[0137] The sniffing analysis module is also used to analyze the grayscale image corresponding to the electromagnetic signal to be analyzed based on the optimized big data model to obtain the corresponding sniffing result.

[0138] Furthermore, the signal acquisition module is also used to control the reference device to execute a preset operation instruction to acquire a reference electromagnetic signal to be analyzed radiated from the screen surface of the reference device;

[0139] The image conversion module is also used to convert the reference electromagnetic signal to be analyzed into a corresponding grayscale image;

[0140] The model optimization module is also used to construct a model optimization set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction;

[0141] The model optimization module is also used to optimize the big data model based on the optimization to obtain the optimized big data model.

[0142] It should be noted that the device for simulating sniffing attacks on the screen of a mobile device mentioned in the second aspect is similar to the technical principles of the method for simulating sniffing attacks on the screen of a mobile device mentioned in the first aspect in terms of technical issues, technical means and technical effects, and will not be elaborated here.

[0143] In the description of the present application, it should be noted that the terms "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0144] It should be noted that, in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0145] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A method for simulating sniffing attack on a mobile device screen, characterized in that: The method comprises the following steps: Collect the electromagnetic signals to be analyzed radiated from the screen surface of the target device; Converting the electromagnetic signal to be analyzed into a corresponding grayscale image; Based on the preset big data model, the grayscale image corresponding to the electromagnetic signal to be analyzed is analyzed to obtain the corresponding sniffing result.

2. The method for simulating sniffing attack on a mobile device screen as claimed in claim 1, characterized in that: The method also includes a big data model training process, and the big data model training process includes the following steps: Controlling the reference device to execute preset operation instructions to collect reference electromagnetic signals to be analyzed radiated from the screen surface of the reference device; Converting the reference electromagnetic signal to be analyzed into a corresponding grayscale image; Constructing a model training set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction; Based on the model training set, the big data model is trained and obtained.

3. The method for simulating sniffing attack on a mobile device screen as claimed in claim 2, characterized in that: Constructing a model training set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction includes the following steps: Segmenting the grayscale image corresponding to the reference electromagnetic signal to be analyzed to obtain a corresponding segmented grayscale image; Performing a convolution operation based on the segmented grayscale images corresponding to each of the grayscale images to obtain a convolution result corresponding to each of the grayscale images; Based on the segmented grayscale images corresponding to each of the grayscale images, a pooling operation is performed to obtain a pooling result corresponding to each of the grayscale images; A model training set is constructed based on the convolution result, the pooling result and the preset operation instruction corresponding to the reference electromagnetic signal to be analyzed.

4. The method for simulating sniffing attack on a mobile device screen as claimed in claim 1, characterized in that: The method further comprises the following steps: Optimizing the big data model to obtain an optimized big data model; Based on the optimized big data model, the grayscale image corresponding to the electromagnetic signal to be analyzed is analyzed to obtain the corresponding sniffing result.

5. The method for simulating sniffing attack on a mobile device screen as claimed in claim 4, characterized in that: Optimizing the big data model to obtain an optimized big data model includes the following steps: Controlling the reference device to execute preset operation instructions to collect reference electromagnetic signals to be analyzed radiated from the screen surface of the reference device; Converting the reference electromagnetic signal to be analyzed into a corresponding grayscale image; Constructing a model optimization set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction; Based on the optimization of the big data model, the optimized big data model is obtained.

6. A device for simulating sniffing attacks on mobile device screens, characterized in that: The device comprises: A signal acquisition module, which is used to collect electromagnetic signals to be analyzed radiated from the screen surface of the target device; An image conversion module, which is used to convert the electromagnetic signal to be analyzed into a corresponding grayscale image; The sniffing analysis module is used to analyze the grayscale image corresponding to the electromagnetic signal to be analyzed based on a preset big data model to obtain the corresponding sniffing result.

7. The device for simulating sniffing attack on the screen of a mobile device as claimed in claim 6, characterized in that: The device also includes a model training module; The signal acquisition module is also used to control the reference device to execute a preset operation instruction to acquire a reference electromagnetic signal to be analyzed radiated from the screen surface of the reference device; The image conversion module is also used to convert the reference electromagnetic signal to be analyzed into a corresponding grayscale image; The model training module is used to construct a model training set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction; The model training module is also used to train and obtain the big data model based on the model training set.

8. The device for simulating sniffing attack on the screen of a mobile device as claimed in claim 7, characterized in that: The device also includes a convolution analysis module and a pooling analysis module; The image conversion module is also used to segment the grayscale image corresponding to the reference electromagnetic signal to be analyzed to obtain a corresponding segmented grayscale image; The convolution analysis module is used to perform a convolution operation based on the segmented grayscale image corresponding to each of the grayscale images to obtain a convolution result corresponding to each of the grayscale images; The pooling analysis module is used to perform a pooling operation based on the segmented grayscale images corresponding to each of the grayscale images to obtain a pooling result corresponding to each of the grayscale images; The model training module is also used to construct a model training set based on the convolution result, the pooling result and the preset operation instruction corresponding to the reference electromagnetic signal to be analyzed.

9. The device for simulating sniffing attack on the screen of a mobile device as claimed in claim 8, characterized in that: The device also includes a model optimization module; The model optimization module is used to optimize the big data model to obtain an optimized big data model; The sniffing analysis module is also used to analyze the grayscale image corresponding to the electromagnetic signal to be analyzed based on the optimized big data model to obtain the corresponding sniffing result.

10. The device for simulating sniffing attack on the screen of a mobile device as claimed in claim 9, characterized in that: The signal acquisition module is also used to control the reference device to execute a preset operation instruction to acquire a reference electromagnetic signal to be analyzed radiated from the screen surface of the reference device; The image conversion module is also used to convert the reference electromagnetic signal to be analyzed into a corresponding grayscale image; The model optimization module is also used to construct a model optimization set based on the grayscale image corresponding to the reference electromagnetic signal to be analyzed and the preset operation instruction; The model optimization module is also used to optimize the big data model based on the optimization to obtain the optimized big data model.

Images