Data storage security level protection evaluation method and system

Abstract

The invention discloses a data storage security level protection evaluation method and system. The evaluation method comprises the following steps: S1, acquiring security related data from a plurality of data storage nodes; s2, performing cleaning and format conversion on the collected data to obtain preprocessed data; s3, according to the preprocessed data, scoring key safety evaluation indexes by using a fuzzy logic reasoning model, and generating a safety score through a normalization calculation step; s4, potential security threats are analyzed, and a risk prediction result is generated; and S5, generating a personalized safety protection suggestion according to the score and the risk prediction result. And dynamically evaluating the security level of data storage.

Description

Technical Field

[0001] The present invention relates to the field of security assessment technology, and in particular to a data storage security level protection assessment method and system. Background Art

[0002] With the rapid development of information technology, data storage systems have become an important part of enterprises, governments and individuals, and data storage security has become a core issue that needs to be addressed. Existing data storage security assessment systems mostly use rule-based or static assessment methods to score the security of data storage environments and provide protection recommendations. However, these traditional systems have the following problems:

[0003] 1. Low data collection efficiency: Existing technologies mostly rely on centralized data collection methods. When faced with large-scale data storage nodes, the collection efficiency is low, especially in distributed storage environments, where it is difficult to collect complete security-related data in real time.

[0004] 2. Insufficient data cleaning and processing accuracy: The collected security data is often mixed with a large amount of noise and invalid data. Traditional data cleaning algorithms are relatively simple and cannot effectively filter out abnormal data, resulting in inaccurate subsequent analysis results.

[0005] 3. Security assessment lacks dynamism: Existing technologies often use static assessment models, which cannot dynamically adjust the data storage security level according to different scenarios and real-time conditions. The accuracy and practicality of the assessment are poor, especially when dealing with new threats.

[0006] 4. Insufficient risk prediction capabilities: Existing systems usually conduct risk assessments based on historical data and simple statistical models. They lack the ability to effectively predict potential threats in the future and find it difficult to identify possible security incidents in advance.

[0007] 5. Lack of personalization in protection recommendations: The security protection recommendations provided by traditional systems are relatively general and cannot generate personalized security protection strategies based on specific security assessment results and risk prediction results. Summary of the invention

[0008] In view of the above problems, the present invention is proposed to provide a data storage security level protection evaluation method and system that overcomes the above problems or at least partially solves the above problems.

[0009] According to one aspect of the present invention, a data storage security level protection evaluation method is provided, the evaluation method comprising:

[0010] Step S1: Collect security-related data from multiple data storage nodes;

[0011] Step S2: Cleaning and formatting the collected data to obtain preprocessed data;

[0012] Step S3: Based on the preprocessed data, the key safety assessment indicators are scored using a fuzzy logic reasoning model, and a safety score is generated through a normalized calculation step;

[0013] Step S4: Analyze potential security threats and generate risk prediction results;

[0014] Step S5: Generate personalized security protection recommendations based on the score and the risk prediction result.

[0015] Optionally, the security-related data specifically includes: system access logs, vulnerability information and permission allocation.

[0016] Optionally, the step S1: collecting security-related data from multiple data storage nodes specifically includes:

[0017] Adopt distributed data collection architecture, improve data transmission efficiency through optimized data compression algorithm, and according to the formula:

[0018]

[0019] Among them, D comp is the amount of compressed data, D raw is the original data volume, C eff is the compression efficiency coefficient.

[0020] Optionally, the step S2: cleaning and format conversion of the collected data to obtain pre-processed data specifically includes:

[0021] Use the density-based noise identification algorithm DBSCAN to identify and filter outliers in the data;

[0022] The data is normalized and formatted using a parallel processing framework.

[0023] Optionally, the step S3: scoring the key safety assessment indicators using a fuzzy logic reasoning model according to the preprocessed data, and generating a safety score through a normalized calculation step specifically includes:

[0024] Set key security assessment indicators I = {I_1, I_2, ..., I_n} and assign weights to each indicator;

[0025] By evaluating the function E j (I j ) Each indicator is scored separately;

[0026] According to the reasoning results of the multi-layer fuzzy reasoning model, the overall safety level score S is calculated by the weighted summation formula total ,.

[0027] Optionally, the evaluation function E j (I j ) automatically adjusts parameters based on a variety of scenarios and indicator types.

[0028] Optionally, the step S4: analyzing potential security threats and generating risk prediction results specifically includes:

[0029] Use the long short-term memory network (LSTM) model to train the historical logs of data storage to obtain a prediction model for potential threats;

[0030] Input new data to predict possible security incidents;

[0031] Similar security risks are classified based on clustering algorithms and risk feature maps are generated.

[0032] Optionally, the step S5: generating personalized security protection suggestions according to the score and the risk prediction result specifically includes:

[0033] Based on the safety rating S total , generate preliminary security protection strategies;

[0034] Adjust the parameters in the protection strategy based on the prediction results of the risk analysis module;

[0035] Through the optimization algorithm, the best solution is selected from multiple protection solutions to obtain a security recommendation report.

[0036] Optionally, the formula for generating personalized security protection suggestions is:

[0037]

[0038] Among them, R j is the jth suggestion, A is the set of suggestions, P(A|S total , R pred ) is the probability function for generating suggestions, S total R is the overall safety rating. pred The risk prediction results are generated.

[0039] The present invention also provides a data storage security level protection evaluation system, which uses the above-mentioned data storage security level protection evaluation method, and the evaluation system includes:

[0040] A data collection module, used for collecting security-related data from multiple data storage nodes;

[0041] The preprocessing module is used to clean and convert the collected data to obtain preprocessed data;

[0042] A safety level assessment module, used to score key safety assessment indicators based on the preprocessed data using a fuzzy logic reasoning model, and generate a safety score through a normalized calculation step;

[0043] Risk analysis module, used to analyze potential security threats and generate risk prediction results;

[0044] The security advice generation module is used to generate personalized security protection advice based on the score and the risk prediction result.

[0045] The present invention provides a data storage security level protection evaluation method and system, the evaluation method comprises: step S1: collecting security-related data from multiple data storage nodes; step S2: cleaning and format conversion of the collected data to obtain pre-processed data; step S3: scoring key security evaluation indicators using a fuzzy logic reasoning model based on the pre-processed data, and generating a security score through a normalization calculation step; step S4: analyzing potential security threats and generating risk prediction results; step S5: generating personalized security protection recommendations based on the score and the risk prediction results. Dynamically evaluate the security level of data storage.

[0046] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0048] Figure 1 A flowchart of a data storage security level protection evaluation method provided by an embodiment of the present invention;

[0049] Figure 2 A block diagram of the composition of a data storage security level protection evaluation system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0051] The terms "comprises" and "having" and any variations thereof in the description embodiments, claims and drawings of the present invention are intended to cover non-exclusive inclusions, for example, including a series of steps or units.

[0052] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0053] like Figure 1 As shown, a data storage security level protection evaluation method dynamically evaluates the security level of data storage, including:

[0054] S1. Collect security-related data such as system access logs, vulnerability information, and permission allocation from multiple data storage nodes through the data collection module;

[0055] S2. Clean and format the collected data through the preprocessing module;

[0056] S3. Based on the preprocessed data, the fuzzy logic reasoning model in the security level assessment module is used to score the key security assessment indicators;

[0057] S4. generating an overall safety score through a normalization calculation step;

[0058] S5. Analyze potential security threats through the risk analysis module and generate risk prediction results;

[0059] S6. Generate personalized security protection recommendations based on the scoring and prediction results.

[0060] The data acquisition module adopts a distributed data acquisition architecture, improves data transmission efficiency through an optimized data compression algorithm, and according to the formula:

[0061]

[0062] Among them, D comp is the amount of compressed data, D raw is the original data volume, C eff To compress the efficiency coefficient, ensure that security-related data can be efficiently collected on large-scale nodes.

[0063] In step S2, the preprocessing module completes data processing through the following steps:

[0064] S2a. Use the density-based noise identification algorithm DBSCAN to identify and filter outliers in the data;

[0065] S2b. Normalize and format the data through a parallel processing framework.

[0066] In step S3, the security level assessment module completes the security level assessment through the following steps:

[0067] S3a. Set key security assessment indicators I = {I_1, I_2, ..., I_n} and assign weights to each indicator;

[0068] S3b. By evaluating the function E j (I j ) Each indicator is scored separately;

[0069] S3c. According to the reasoning results of the multi-layer fuzzy reasoning model, the overall safety level score S is calculated by the weighted summation formula total .

[0070] Evaluation function E j (I j ) automatically adjusts parameters according to different scenarios and indicator types.

[0071] In step S5, the risk analysis module completes the risk analysis by the following steps:

[0072] S5a. Use the long short-term memory network (LSTM) model to train the historical logs of the data storage to obtain a prediction model for potential threats;

[0073] S5b. Input new data and predict possible security incidents;

[0074] S5c. Classify similar security risks based on clustering algorithm and generate risk feature map.

[0075] The clustering algorithm uses K-Means or hierarchical clustering method to perform cluster analysis on risk characteristics, form different risk levels and types, and generate corresponding risk handling recommendations.

[0076] The security advice generation module generates security protection advice through the following steps:

[0077] S6a. Based on the safety level score S total Generate preliminary security protection strategies;

[0078] S6b. Adjust the parameters in the protection strategy based on the prediction results of the risk analysis module;

[0079] S6c. Select the best solution from multiple protection solutions through optimization algorithm and output a security recommendation report.

[0080] The security suggestion generation module generates security suggestions according to the following formula:

[0081]

[0082] Among them, R j is the jth suggestion, A is the set of suggestions, P(A|S total , R pred ) is the probability function for generating suggestions, S total R is the overall safety rating. pred The risk prediction results generated by the risk analysis module.

[0083] The system provides an adaptive parameter adjustment function, allowing users to dynamically adjust the weight parameter Wj of the key evaluation indicator according to the formula:

[0084]

[0085] Among them, S i is the safety score of the i-th evaluation indicator, W j is the weight, E j (I j ) is the evaluation function, which enables the system to adapt to security demand assessment in different scenarios and dynamically generate security level scores.

[0086] like Figure 2 As shown, a data storage security level protection evaluation system includes a data acquisition module, a preprocessing module, a security level evaluation module, a risk analysis module and a security suggestion generation module.

[0087] Beneficial effects: The present invention adopts a distributed data collection architecture and combines it with an optimized data compression algorithm to greatly improve the efficiency of large-scale node data collection, and can collect security-related data from multiple data storage nodes in real time, effectively solving the problem of low collection efficiency in the prior art;

[0088] The present invention filters abnormal data through the density-based noise identification algorithm DBSCAN, and cleans and normalizes the data through a parallel processing framework to ensure the accuracy of subsequent evaluation data. This effectively solves the problem of insufficient data cleaning and processing accuracy in existing systems;

[0089] The present invention adopts a fuzzy logic reasoning model, which can dynamically adjust the security level of data storage according to different scenarios and real-time security data, provide accurate security level assessment, overcome the limitations of the static assessment model in the prior art, and improve the accuracy of the assessment;

[0090] The present invention uses a deep learning model based on LSTM (Long Short-Term Memory Network) to analyze historical security logs stored in data and predict possible future security threats, thereby improving the risk identification and prediction capabilities of the system and making up for the lack of future threat prediction capabilities in the prior art.

[0091] The present invention generates personalized security protection suggestions based on security assessment results and risk prediction results, adopts an optimization algorithm to select the best solution from multiple protection solutions, and provides customized security suggestions, thereby solving the problem that protection suggestions in the prior art lack pertinence and personalization.

[0092] The above specific implementation methods further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A data storage security level protection evaluation method, characterized in that: The evaluation method includes: Step S1: Collect security-related data from multiple data storage nodes; Step S2: Cleaning and formatting the collected data to obtain preprocessed data; Step S3: Based on the preprocessed data, the key safety assessment indicators are scored using a fuzzy logic reasoning model, and a safety score is generated through a normalized calculation step; Step S4: Analyze potential security threats and generate risk prediction results; Step S5: Generate personalized security protection recommendations based on the score and the risk prediction result.

2. A data storage security level protection evaluation method according to claim 1, characterized in that: The security-related data specifically includes: system access logs, vulnerability information and permission allocation.

3. A data storage security level protection evaluation method according to claim 1, characterized in that: The step S1: collecting security-related data from multiple data storage nodes specifically includes: Adopt distributed data collection architecture, improve data transmission efficiency through optimized data compression algorithm, and according to the formula: Among them, D comp is the amount of compressed data, D raw is the original data volume, C eff is the compression efficiency coefficient.

4. A data storage security level protection evaluation method according to claim 1, characterized in that: The step S2: cleaning and format conversion of the collected data to obtain pre-processed data specifically includes: Use the density-based noise identification algorithm DBSCAN to identify and filter outliers in the data; The data is normalized and formatted using a parallel processing framework.

5. A data storage security level protection evaluation method according to claim 1, characterized in that: The step S3: scoring the key safety assessment indicators using the fuzzy logic inference model according to the pre-processed data, and generating the safety score through the normalization calculation step specifically includes: Set key security assessment indicators I = {I_1, I_2, ..., I_n} and assign weights to each indicator; By evaluating the function E j (I j ) Each indicator is scored separately; According to the reasoning results of the multi-layer fuzzy reasoning model, the overall safety level score S is calculated by the weighted summation formula total .

6. A data storage security level protection evaluation method according to claim 5, characterized in that: The evaluation function E j (I j ) automatically adjusts parameters based on a variety of scenarios and indicator types.

7. A data storage security level protection evaluation method according to claim 1, characterized in that: The step S4: analyzing potential security threats and generating risk prediction results specifically includes: Use the long short-term memory network (LSTM) model to train the historical logs of data storage to obtain a prediction model for potential threats; Input new data to predict possible security incidents; Similar security risks are classified based on clustering algorithms and risk feature maps are generated.

8. A data storage security level protection evaluation method according to claim 1, characterized in that: The step S5: generating personalized security protection suggestions according to the score and the risk prediction result specifically includes: Based on the safety rating S total Generate preliminary security protection strategies; Adjust the parameters in the protection strategy based on the prediction results of the risk analysis module; Through the optimization algorithm, the best solution is selected from multiple protection solutions to obtain a security recommendation report.

9. A data storage security level protection evaluation method according to claim 1, characterized in that: The formula for generating personalized security protection recommendations is: Among them, R j is the jth suggestion, A is the set of suggestions, P(A|S total ,R pred ) is the probability function for generating suggestions, S total R is the overall safety rating. pred The risk prediction results are generated.

10. A data storage security level protection evaluation system, using a data storage security level protection evaluation method according to any one of claims 1 to 9, characterized in that: The evaluation system comprises: A data collection module, used for collecting security-related data from multiple data storage nodes; The preprocessing module is used to clean and convert the collected data to obtain preprocessed data; A safety level assessment module, used to score key safety assessment indicators based on the preprocessed data using a fuzzy logic reasoning model, and generate a safety score through a normalized calculation step; Risk analysis module, used to analyze potential security threats and generate risk prediction results; The security advice generation module is used to generate personalized security protection advice based on the score and the risk prediction result.

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