Employment platform network vulnerability detection method, device, equipment, medium and product
By disassembling and feature extraction of network traffic data of the employment platform and combining with machine learning models, network vulnerability types are identified, which solves the problem of insufficient robustness and accuracy of network vulnerability detection in the existing technology, and achieves more efficient network security threat detection.
Patent Information
- Application Number
- CN202411914090.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-24
- Publication Date
- 2025-03-25
AI Technical Summary
The prior art is difficult to improve the robustness, generalization capability and accuracy of network vulnerability detection, especially in complex wireless communication networks.
By obtaining network traffic data of the employment platform, disassembly and preprocessing the software object code, extracting network vulnerability feature sequences, and identifying network vulnerability types based on machine learning models.
It improves the robustness, generalization ability and accuracy of network vulnerability detection, reduces model complexity and computing resource consumption, and enhances the detection ability of network security threats.
Smart Images
Figure CN119696913A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of network vulnerability detection, and in particular to a method, device, equipment, medium and product for detecting network vulnerability of an employment platform. Background Art
[0002] With the increasing popularity and rapid development of wireless communication networks, more and more people have become inseparable from wireless communication. However, as the scale of the network grows, network threats are also escalating. Network attacks have become an increasing threat, and network security has become a top priority for wireless communication networks.
[0003] Vulnerability monitoring is crucial for wireless communication network security. However, modern computer networks are very complex, which makes them vulnerable to various attacks. Vulnerability attacks in the network can be achieved through network vulnerability scanners and vulnerability attacks, and there are many ways to spread and infect computers. Computer network security vulnerabilities can have a serious impact on user information and data, and even affect the normal operation of the entire network. Therefore, the tracking and prevention of computer network security vulnerabilities should be strengthened.
[0004] In summary, how to improve the robustness, generalization ability and accuracy of network vulnerability detection has become a problem that needs to be solved urgently. Summary of the invention
[0005] The purpose of this application is to provide a method, device, equipment, medium and product for detecting network vulnerabilities of employment platforms, which can improve the robustness, generalization ability and accuracy of network vulnerability detection of employment platforms.
[0006] To achieve the above objectives, this application provides the following solutions:
[0007] In a first aspect, the present application provides a method for detecting network vulnerabilities of an employment platform, comprising:
[0008] Obtain network traffic data of the target employment platform;
[0009] Preprocessing the network traffic data of the target employment platform by using the disassembly of the software target code to obtain the disassembly code of the target employment platform;
[0010] Extracting the network vulnerability feature sequence of the target employment platform from the disassembled code of the target employment platform;
[0011] Based on the network vulnerability feature sequence and network vulnerability detection model of the target employment platform, the network vulnerability type of the target employment platform is obtained; wherein the network vulnerability detection model is constructed based on a machine learning model.
[0012] Optionally, the method for determining the network vulnerability detection model includes:
[0013] Obtain network traffic data of different employment platforms;
[0014] The network traffic data of each employment platform is preprocessed by disassembling the software target code to obtain the disassembly code used for training;
[0015] Extracting a network vulnerability feature sequence from the disassembled code used for training to obtain a network vulnerability feature sequence used for training;
[0016] Perform sequence clustering on the network vulnerability feature sequences used for training to obtain the network vulnerability types corresponding to the network vulnerability feature sequences used for training;
[0017] The machine learning model is trained based on the network vulnerability feature sequence used for training and the corresponding network vulnerability type to obtain the network vulnerability detection model.
[0018] Optionally, based on the network vulnerability feature sequence and network vulnerability detection model of the target employment platform, the network vulnerability type of the target employment platform is obtained, specifically including:
[0019] Quantify the network vulnerability feature sequence of the target employment platform to obtain the digital feature vector of the target employment platform;
[0020] Based on the digital feature vector of the target employment platform and the network vulnerability detection model, the network vulnerability type of the target employment platform is obtained.
[0021] Optionally, based on the digital feature vector of the target employment platform and the network vulnerability detection model, the network vulnerability type of the target employment platform is obtained, specifically including:
[0022] Performing data processing on the digital feature vector of the target employment platform to obtain a processed digital feature vector of the target employment platform; the data processing includes: filtering useless data and filling missing values;
[0023] The digital feature vector processed by the target employment platform is input into the network vulnerability detection model to obtain the network vulnerability type of the target employment platform.
[0024] Optionally, the machine learning model is a naive Bayes model, a decision tree model, a support vector machine or a random forest model.
[0025] Optionally, performing sequence clustering on the network vulnerability feature sequence used for training to obtain the network vulnerability type corresponding to the network vulnerability feature sequence used for training specifically includes:
[0026] The K-Means algorithm, the hierarchical clustering algorithm or the density clustering algorithm are used to perform sequence clustering on the network vulnerability feature sequence used for training, so as to obtain the network vulnerability type corresponding to the network vulnerability feature sequence used for training.
[0027] In a second aspect, the present application provides a device for detecting network vulnerabilities of an employment platform, comprising:
[0028] A data acquisition module, used to acquire network traffic data of a target employment platform;
[0029] A data processing module, used for pre-processing the network traffic data of the target employment platform by disassembling the software target code, and obtaining the disassembly code of the target employment platform;
[0030] A feature extraction module, used to extract a network vulnerability feature sequence of a target employment platform from the disassembled code of the target employment platform;
[0031] The network vulnerability detection module is used to obtain the network vulnerability type of the target employment platform based on the network vulnerability feature sequence and network vulnerability detection model of the target employment platform; wherein the network vulnerability detection model is constructed based on a machine learning model.
[0032] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the employment platform network vulnerability detection methods described above.
[0033] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described employment platform network vulnerability detection methods.
[0034] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned employment platform network vulnerability detection methods.
[0035] According to the specific embodiments provided in this application, this application has the following technical effects:
[0036] The present application provides a method, apparatus, equipment, medium and product for detecting network vulnerabilities of an employment platform. The network traffic data of a target employment platform is preprocessed by disassembling the software target code to obtain the disassembly code of the target employment platform, and the network vulnerability feature sequence of the target employment platform is extracted from the disassembly code of the target employment platform. The network vulnerability type of the target employment platform is obtained based on the network vulnerability feature sequence of the target employment platform and a network vulnerability detection model constructed based on a machine learning model. Through effective feature extraction and selection, the performance of the machine learning model can be improved, the model complexity and computing resource consumption can be reduced, and the robustness and generalization ability of the model to network security threats can be enhanced with high accuracy. The present application realizes the detection of network vulnerability types based on a machine learning model, which can improve the robustness, generalization ability and accuracy of employment platform network vulnerability detection. BRIEF DESCRIPTION OF THE DRAWINGS
[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in 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 paying creative work.
[0038] Figure 1 This is an application environment diagram of a method for detecting network vulnerabilities of an employment platform in one embodiment of the present application;
[0039] Figure 2 A flowchart of a method for detecting network vulnerabilities of an employment platform provided in one embodiment of the present application;
[0040] Figure 3 A flowchart of a method for determining a network vulnerability detection model provided in an embodiment of the present application;
[0041] Figure 4 A schematic diagram of network vulnerability detection time comparison provided in an embodiment of the present application;
[0042] Figure 5 A schematic diagram of network vulnerability detection accuracy provided by an embodiment of the present application;
[0043] Figure 6 A schematic diagram of functional modules of a device for detecting network vulnerabilities of an employment platform provided by another embodiment of the present application;
[0044] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only 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 creative work are within the scope of protection of this application.
[0046] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.
[0047] The employment platform network vulnerability detection method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the network traffic data of the target employment platform to the server 104. After the server 104 receives the network traffic data of the target employment platform, for the network traffic data of the target employment platform, the server 104 pre-processes the network traffic data of the target employment platform by disassembling the software target code to obtain the disassembly code of the target employment platform; extracts the network vulnerability feature sequence of the target employment platform from the disassembly code of the target employment platform; based on the network vulnerability feature sequence of the target employment platform and the network vulnerability detection model, obtains the network vulnerability type of the target employment platform; wherein the network vulnerability detection model is built based on the machine learning model. The server 104 can feedback the obtained network vulnerability type of the target employment platform to the terminal 102. In addition, in some embodiments, the employment platform network vulnerability detection method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the network traffic data of the target employment platform, or the server 104 can obtain the network traffic data of the target employment platform from the data storage system and process the network traffic data of the target employment platform.
[0048] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.
[0049] In an exemplary embodiment, Figure 2As shown, a method for detecting network vulnerabilities of an employment platform is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 204. Among them:
[0050] Step 201, obtaining network traffic data of the target employment platform.
[0051] Step 202, pre-processing the network traffic data of the target employment platform by using the disassembly of the software target code to obtain the disassembly code of the target employment platform.
[0052] Step 203, extracting the network vulnerability feature sequence of the target employment platform from the disassembled code of the target employment platform.
[0053] Step 204, based on the network vulnerability feature sequence and network vulnerability detection model of the target employment platform, obtain the network vulnerability type of the target employment platform; wherein the network vulnerability detection model is constructed based on a machine learning model.
[0054] Implementing the above steps 201 to 204 can improve the robustness, generalization ability and accuracy of employment platform network vulnerability detection.
[0055] In another exemplary embodiment of the present application, see Figure 3 , the method for determining the network vulnerability detection model includes:
[0056] Step 301, obtaining network traffic data of different employment platforms.
[0057] Step 302 , pre-processing the network traffic data of each employment platform by disassembling the software target code to obtain the disassembly code for training.
[0058] Step 303: extracting a network vulnerability feature sequence from the decompiled code used for training to obtain a network vulnerability feature sequence used for training.
[0059] Step 304: cluster the network vulnerability feature sequences used for training to obtain the network vulnerability types corresponding to the network vulnerability feature sequences used for training. Specifically: cluster the network vulnerability feature sequences used for training using a K-Means algorithm, a hierarchical clustering algorithm, or a density clustering algorithm to obtain the network vulnerability types corresponding to the network vulnerability feature sequences used for training.
[0060] Step 305: Train the machine learning model based on the network vulnerability feature sequence used for training and the corresponding network vulnerability type to obtain the network vulnerability detection model.
[0061] In another exemplary embodiment of the present application, step 204 specifically includes:
[0062] (1) Quantify the network vulnerability feature sequence of the target employment platform to obtain the digital feature vector of the target employment platform.
[0063] (2) Based on the digital feature vector and network vulnerability detection model of the target employment platform, the network vulnerability type of the target employment platform is obtained. Specifically: Based on the digital feature vector and network vulnerability detection model of the target employment platform, the network vulnerability type of the target employment platform is obtained, including:
[0064] The digital feature vector of the target employment platform is processed to obtain the processed digital feature vector of the target employment platform; the data processing includes: filtering useless data and filling missing values; the processed digital feature vector of the target employment platform is input into a network vulnerability detection model to obtain the network vulnerability type of the target employment platform.
[0065] In another exemplary embodiment of the present application, the machine learning model is a naive Bayes model, a decision tree model, a support vector machine or a random forest model.
[0066] In another exemplary embodiment of the present application, after the network vulnerability type of the target employment platform is detected in step 204, the employment platform network vulnerability detection method further includes: determining that there is a suspicious network threat on the target employment platform, sending an alarm to notify the network administrator so that the network administrator can take timely measures.
[0067] In practical applications, a more specific implementation process of the above-mentioned employment platform network vulnerability detection method can be described as follows.
[0068] Step 1: Collect network traffic data and store it in a database. The data stored in the database is preprocessed before sequence clustering.
[0069] Step 2: Extract features from the data collected in step 1 (i.e., extract digital feature vectors), and perform feature training to learn classification rules and classify new data samples into different categories.
[0070] Step 3: Preprocess the data in step 2 to make it suitable for the machine learning model. The preprocessing operations include filtering useless data and filling missing values.
[0071] Step 4: Input the data in step 3 into the machine learning model to build a network vulnerability detection model, and use the machine learning model to classify and learn the extracted features so that the types of vulnerabilities in the network can be identified.
[0072] Step 5: Use the trained machine learning model to monitor network threats in real time and realize intelligent monitoring of the network environment. When the system detects suspicious network threats, it can send an alarm to notify the network administrator so that the network administrator can take timely measures.
[0073] Among them, in step 1, the network traffic data is preprocessed by disassembling the software target code to form a disassembled code that is easier to understand and analyze. Then, the code that generates the software vulnerability is extracted from the disassembled code to obtain the network vulnerability feature sequence. The sequence clustering algorithm includes the K-Means algorithm, the hierarchical clustering algorithm, the density clustering algorithm, etc. Sequence clustering is to divide data samples with similar characteristics into different clusters, so that the data samples in the same cluster have similarity, and the data samples in different clusters have differences. Attack behaviors with similar characteristics are classified into one category, so as to discover new attack modes or attack variants.
[0074] Among them, in step 2, after extracting the feature information, a network vulnerability feature sequence database is established. When there is a vulnerability to be detected, the vulnerability type is determined by comparing it with the features of the vulnerability in the database. In the network vulnerability feature sequence database, the vulnerability is stored in the form of a sequence, and each sequence consists of the basic events that make up the vulnerability.
[0075] In sequence clustering, the defect sequence is a set of instructions that cause network defects extracted from the network disassembly code. It is a series of software vulnerability feature sequences. The defect sequence database is the network vulnerability feature sequence database. When the network vulnerability sequence database is created, the vulnerability sequence type is represented by defining the vulnerability parent class and vulnerability child class, and the assembly code segment is represented by defining the atomic code class, thereby generating the behavior that generates the vulnerability. In step 2, feature quantization is used to extract features. Its feature quantization is to convert the original features into feature vectors that can be analyzed by data analysis packages or machine learning algorithms. Digital features can be directly used as program input without quantization, while classification and text features need to be converted into digital features.
[0076] When clustering sequences, specifically, the features of the network vulnerability feature sequences in the database are compared to obtain the overall similarity value of the network vulnerability feature sequences. The calculation formula is:
[0077] CLSM(S1,S2)=q×LSim(S1,S2);
[0078] Among them, S1 and S2 represent two network vulnerability feature sequences, and q represents the parameter similarity measurement parameter. CLSM represents the calculation result or measure; the LSim function is for the linear time-invariant model, given any input, to obtain any output. The response of a continuous system to an arbitrary input function can be obtained using the LSim function. In two network vulnerability feature sequences, if the basic events are the same but the order of the basic events is different, the similarity between them will decrease. On the contrary, if the order of the basic events of a part of the two sequences is the same, and the basic events that make up the sequence are also very different, then the similarity between the two sequences may also be very low, indicating different types of vulnerabilities. The low entropy sequence that is easy to attack is most likely to become the center of the sequence clustering. In this way, the calculation of the vulnerability sequence similarity is completed through the above process, providing a basic basis for network vulnerability detection.
[0079] Combination Figure 4 It can be seen that as the sliding window increases, the amount of vulnerability data also increases. The method of the present application corresponds to line 1, and the traditional method corresponds to line 2. Figure 4 It can be seen that the vulnerability data detection time of the method of the present application and the traditional method also increases linearly. After comparison, it can be seen that the detection time of the detection method of the present application is less than the detection time of the traditional method.
[0080] Combination Figure 5 It can be seen that in the case of small data volume, the accuracy of the static vulnerability detection method based on the machine learning model is not very high. Due to the auxiliary information required for the initial analysis, the error is large. However, as the amount of information increases, the error gradually decreases, so the accuracy of vulnerability detection is higher. The method of this application corresponds to the broken line 1, and the traditional method corresponds to the broken line 2. Figure 5 The comparison in shows that the detection accuracy of the method of the present application is higher than that of the traditional method.
[0081] The embodiment of the present application implements employment platform network vulnerability detection based on machine learning. Through effective feature extraction and selection, it can improve the performance of the machine learning model, reduce model complexity and computing resource consumption, and enhance the model's robustness and generalization capabilities against network security threats. By utilizing big data analysis technology, valuable information can be extracted from massive data and used for the training and evaluation of machine learning models, thereby improving the accuracy and robustness of machine learning models.
[0082] Other parts not described in detail in this embodiment belong to the prior art and will not be described here.
[0083] Based on the same inventive concept, the embodiment of the present application also provides an employment platform network vulnerability detection device for implementing the employment platform network vulnerability detection method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more employment platform network vulnerability detection device embodiments provided below can refer to the limitations of the employment platform network vulnerability detection method above, and will not be repeated here.
[0084] In an exemplary embodiment, Figure 6 As shown, a device for detecting network vulnerabilities of an employment platform is provided, comprising:
[0085] The data acquisition module 601 is used to acquire the network traffic data of the target employment platform.
[0086] The data processing module 602 is used to pre-process the network traffic data of the target employment platform by disassembling the software target code to obtain the disassembly code of the target employment platform.
[0087] The feature extraction module 603 is used to extract the network vulnerability feature sequence of the target employment platform from the disassembled code of the target employment platform.
[0088] The network vulnerability detection module 604 is used to obtain the network vulnerability type of the target employment platform based on the network vulnerability feature sequence and network vulnerability detection model of the target employment platform; wherein the network vulnerability detection model is constructed based on a machine learning model.
[0089] As an optional implementation, the employment platform network vulnerability detection device further includes: a model determination module, which is used to determine the network vulnerability detection model. The model determination module includes:
[0090] The training data acquisition unit is used to acquire network traffic data of different employment platforms.
[0091] The training data preprocessing unit is used to preprocess the network traffic data of each employment platform by disassembling the software target code to obtain the disassembly code for training.
[0092] The training data feature extraction unit is used to extract a network vulnerability feature sequence from the disassembled code used for training to obtain a network vulnerability feature sequence used for training.
[0093] The sequence clustering unit is used to perform sequence clustering on the network vulnerability feature sequence used for training, and obtain the network vulnerability type corresponding to the network vulnerability feature sequence used for training. Specifically: the K-Means algorithm, the hierarchical clustering algorithm or the density clustering algorithm is used to perform sequence clustering on the network vulnerability feature sequence used for training, and obtain the network vulnerability type corresponding to the network vulnerability feature sequence used for training.
[0094] The model training unit is used to train the machine learning model based on the network vulnerability feature sequence used for training and the corresponding network vulnerability type to obtain the network vulnerability detection model.
[0095] As an optional implementation, the employment platform network vulnerability detection device also includes: an early warning module, which is used to determine whether the target employment platform has a suspicious network threat after detecting the network vulnerability type of the target employment platform, and send an alarm to notify the network administrator so that the network administrator can take timely measures.
[0096] In this embodiment, the training data preprocessing unit, the training data feature extraction unit and the sequence clustering unit are used to process the original features and obtain standard data, which are suitable for training the machine learning algorithm; the model training unit is used to input data into the machine learning model to establish a network vulnerability detection model, and the machine learning model is used for the machine learning algorithm to classify and learn the extracted features so as to identify the vulnerabilities in the network, and to establish a normal behavior model by learning the historical network traffic data, and to detect the newly generated network traffic data to identify abnormal traffic or attack behavior.
[0097] The employment platform network vulnerability detection method or device of the embodiment of the present application collects network traffic data and stores it in a database, pre-processes the data stored in the database and then performs sequence clustering, extracts features from the collected data, learns classification rules through the training data in the features, divides new data samples into different categories, processes the new data to adapt it to the machine learning algorithm, and pre-processes the data (filters useless data and fills missing values). In this way, through effective feature extraction and selection, the performance of the machine learning model can be improved, the model complexity and computing resource consumption can be reduced, and the model's robustness and generalization ability to network security threats can be enhanced, valuable information can be extracted for the training and evaluation of the machine learning model, and the accuracy and robustness of the machine learning model can be improved.
[0098] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store network traffic data of the target employment platform. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for detecting network vulnerabilities of an employment platform is implemented.
[0099] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0100] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.
[0101] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.
[0102] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0103] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0104] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, etc., but is not limited thereto.
[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0106] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A method for detecting network vulnerabilities of an employment platform, characterized in that: The employment platform network vulnerability detection method comprises: Obtain network traffic data of the target employment platform; Preprocessing the network traffic data of the target employment platform by using the disassembly of the software target code to obtain the disassembly code of the target employment platform; Extracting the network vulnerability feature sequence of the target employment platform from the disassembled code of the target employment platform; Based on the network vulnerability feature sequence and network vulnerability detection model of the target employment platform, the network vulnerability type of the target employment platform is obtained; wherein the network vulnerability detection model is constructed based on a machine learning model.
2. The employment platform network vulnerability detection method according to claim 1, characterized in that: The method for determining the network vulnerability detection model includes: Obtain network traffic data of different employment platforms; The network traffic data of each employment platform is preprocessed by disassembling the software target code to obtain the disassembly code used for training; Extracting a network vulnerability feature sequence from the disassembled code used for training to obtain a network vulnerability feature sequence used for training; Perform sequence clustering on the network vulnerability feature sequences used for training to obtain the network vulnerability types corresponding to the network vulnerability feature sequences used for training; The machine learning model is trained based on the network vulnerability feature sequence used for training and the corresponding network vulnerability type to obtain the network vulnerability detection model.
3. The employment platform network vulnerability detection method according to claim 1, characterized in that: Based on the network vulnerability feature sequence and network vulnerability detection model of the target employment platform, the network vulnerability types of the target employment platform are obtained, including: Quantify the network vulnerability feature sequence of the target employment platform to obtain the digital feature vector of the target employment platform; Based on the digital feature vector of the target employment platform and the network vulnerability detection model, the network vulnerability type of the target employment platform is obtained.
4. The employment platform network vulnerability detection method according to claim 3 is characterized in that: Based on the digital feature vector and network vulnerability detection model of the target employment platform, the network vulnerability types of the target employment platform are obtained, including: Performing data processing on the digital feature vector of the target employment platform to obtain a processed digital feature vector of the target employment platform; the data processing includes: filtering useless data and filling missing values; The digital feature vector processed by the target employment platform is input into the network vulnerability detection model to obtain the network vulnerability type of the target employment platform.
5. The employment platform network vulnerability detection method according to claim 1, characterized in that: The machine learning model is a naive Bayes model, a decision tree model, a support vector machine or a random forest model.
6. The employment platform network vulnerability detection method according to claim 2 is characterized in that: The network vulnerability feature sequences used for training are clustered to obtain the network vulnerability types corresponding to the network vulnerability feature sequences used for training, including: The K-Means algorithm, the hierarchical clustering algorithm or the density clustering algorithm are used to perform sequence clustering on the network vulnerability feature sequence used for training, so as to obtain the network vulnerability type corresponding to the network vulnerability feature sequence used for training.
7. A device for detecting network vulnerabilities of an employment platform, characterized in that: The employment platform network vulnerability detection device comprises: A data acquisition module, used to acquire network traffic data of a target employment platform; A data processing module, used for pre-processing the network traffic data of the target employment platform by disassembling the software target code, and obtaining the disassembly code of the target employment platform; A feature extraction module, used to extract a network vulnerability feature sequence of a target employment platform from the disassembled code of the target employment platform; The network vulnerability detection module is used to obtain the network vulnerability type of the target employment platform based on the network vulnerability feature sequence and network vulnerability detection model of the target employment platform; wherein the network vulnerability detection model is constructed based on a machine learning model.
8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the employment platform network vulnerability detection method described in any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for detecting network vulnerabilities of employment platforms described in any one of claims 1 to 6 is implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for detecting network vulnerabilities of employment platforms described in any one of claims 1 to 6 is implemented.