CN115695269B - Comprehensive quantitative evaluation method for performance of fuzzy test tool - Google Patents
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Abstract
The invention provides a comprehensive quantitative evaluation method for performance of a fuzzy test tool, which comprises the following steps of S1: selecting a test benchmark suite and setting experimental conditions; the experimental conditions include test times, overtime time, reference fuzzy test tools and seeds; s2: selecting an evaluation index; the evaluation indexes comprise the number of collapse points, code coverage rate, average time for exposing the collapse points, the number of loopholes, the number of high-risk loopholes and resource occupancy rate; s3: constructing an evaluation model; after forward and normalization processing is carried out on the data, weight is calculated, and then comprehensive quantitative evaluation of the evaluation index is carried out. According to the invention, the comprehensive quantitative evaluation of the performance of the fuzzy test tool is realized by fusing and correlating a plurality of independent evaluation indexes.
Description
Technical Field
The invention relates to the technical field of network space safety, in particular to a comprehensive quantitative evaluation method for performance of a fuzzy test tool.
Background
In the field of network space security, an attacker can use security holes existing in software to cause network security threat and destroy network security. Fuzzy testing is a representative technique among vulnerability detection techniques, which uses a random character stream generated for a target program to perform multiple tests on the target program to detect possible vulnerabilities. The usability of the fuzzy test tool has a direct relation with the improvement of the safety of the tested object, and the evaluation of the fuzzy test tool is beneficial to finding the defects of the fuzzy test tool and inspiring the design of a new fuzzy test tool, so that a unified and standard fuzzy test tool evaluation method is not formed at present.
The current evaluation method for the fuzzy test tool is mainly considered from the two aspects of test conditions and evaluation indexes. The test conditions comprise an experiment platform, a benchmark obfuscator, a benchmark test suite, the number of tests, a vulnerability verification tool, timeout time, the number of experiments, seeds and the like, and the evaluation indexes comprise code coverage rate, tool execution speed, CPU resource occupancy rate, number of collapse points, vulnerability number, coverage rate and the like. The existing evaluation methods for the fuzzy test tools mostly adopt a comparison method, and the advanced performance of the newly designed fuzzy test tools is highlighted by evaluating the performances of the specific reference fuzzy test tools and the newly designed fuzzy test tools.
The emphasis of different fuzzy test tools may be different, such as: AFL is a fuzzy test tool guided by coverage rate, and aims to improve the code coverage rate of a tested object; driller uses a conflic execution to resolve the magic value in order to increase the number of vulnerability detections, more vulnerabilities can be detected than AFL. Therefore, a plurality of independent corresponding values of the evaluation indexes are directly used as the evaluation results of different fuzzy test tools, the evaluation indexes are not effectively fused and associated, and the obtained evaluation results cannot be decided to a reasonable comprehensive quantitative value to represent the performance of the fuzzy test tools.
Disclosure of Invention
In order to solve the problems, the invention provides a comprehensive quantitative evaluation method for the performance of a fuzzy test tool. Based on the selected set experimental conditions and evaluation indexes, carrying out multiple tests on the evaluated fuzzy test tool aiming at each evaluation index to obtain a plurality of numerical values corresponding to each evaluation index; after the data are preprocessed in forward direction, normalization and the like, the weight value of the evaluation index is determined according to the difference degree of the data value of each evaluation index; and finally, constructing a comprehensive quantitative evaluation calculation system, calculating a comprehensive evaluation index quantitative evaluation score, and realizing automatic evaluation calculation of comprehensive evaluation index quantification of the performance of the fuzzy test tool.
The invention provides a comprehensive quantitative evaluation method for performance of a fuzzy test tool, which comprises the following specific technical scheme:
s1: selecting a test benchmark suite and setting experimental conditions;
the experimental conditions comprise test times, overtime time, a reference fuzzy test tool and seeds;
s2: selecting an evaluation index;
the evaluation indexes comprise the number of collapse points, code coverage rate, average time for exposing the collapse points, the number of loopholes, the number of high-risk loopholes and resource occupancy rate;
s3: constructing an evaluation model, and carrying out comprehensive quantitative evaluation on evaluation indexes;
further, in step S3, the comprehensive quantitative evaluation process of the evaluation index is as follows:
s301: constructing an evaluation index numerical matrix;
s302: carrying out data forward processing on the obtained corresponding values of each evaluation index;
s303: carrying out standardization processing on the forward data;
s304: calculating the weight of each evaluation index;
s305: and calculating a comprehensive quantization evaluation value.
Further, in step S301, by performing a plurality of tests on the fuzzy test tool to be tested for each evaluation index, an evaluation index value corresponding to each evaluation index is obtained, and the evaluation index value matrix is constructed, where the evaluation index value matrix is as follows:
X＝(x ij ) (m×n)
wherein m represents the number of tests, and n represents the number of evaluation indexes.
Further, in step S302, the data forward processing is specifically calculated as follows:
wherein ,representing forward data, x j Representing a value set corresponding to a certain evaluation index, x ij Indicating that forward data is required.
Further, in step S303, the normalization process is specifically calculated as follows:
wherein ,yij In order to normalize the data it is,minimum value in column j representing forward data, +.>The maximum value in the j-th column of the forward data is represented by i being {1,2,3 …, m }, and j being {1,2,3 …, n }.
Further, in step S304, the evaluation index weight includes calculating the entropy value of the evaluation index, calculating the difference coefficient and calculating the weight, and the specific process is as follows:
the evaluation index entropy value is calculated as follows:
wherein ,ej Represents the entropy value of the evaluation index, m represents the number of the numerical values corresponding to the evaluation index,representing the proportion of the ith numerical value of a certain evaluation index to the sum of all the numerical values of the evaluation index;
the difference coefficient is calculated as follows:
G j ＝1-e j
wherein ,Gj A difference coefficient representing an evaluation index;
the weight is calculated as follows:
wherein ,Wj And represents the weight of the evaluation index.
Further, in step S305, the comprehensive quantization and evaluation value calculation includes: calculating a weighted normalization matrix; calculating an optimal solution and a worst solution of each evaluation index according to the weighted standardized matrix; and calculating Euclidean distances of the optimal solution and the worst solution of each evaluation index through the weighted standardized matrix, the optimal solution and the worst solution, and finally calculating and outputting the comprehensive quantized evaluation value of the tested fuzzy test tool through the obtained Euclidean distances.
Further, the test benchmark suite is a LAVA-M standard corpus, the test times are 5 times, and the timeout time is 5 hours.
Further, the reference ambiguity test tool is AFLFuzz.
The beneficial effects of the invention are as follows:
the weights of all evaluation indexes are obtained through calculation, and the independent evaluation indexes are associated, so that the automatic quantitative evaluation of comprehensive evaluation index quantification of the performance of the fuzzy test tool is realized, the referenceability of an evaluation result is enhanced, and the evaluation accuracy of the performance of the tested fuzzy test tool is improved.
Drawings
FIG. 1 is a schematic overall flow diagram of the method of the present invention;
FIG. 2 is a schematic diagram of an evaluation index comprehensive quantitative evaluation flow chart of the invention;
FIG. 3 is a flowchart of the comprehensive quantitative evaluation calculation of the present invention.
Detailed Description
In the following description, the technical solutions of the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The embodiment 1 of the invention discloses a comprehensive quantitative evaluation method for the performance of a fuzzy test tool, which is shown in fig. 1, and comprises the following specific steps:
s1: selecting a test benchmark suite and setting experimental conditions;
the test conditions include: number of tests, timeout time, reference fuzzy test tool and seed.
The test benchmark suite is used as an analysis target for fuzzy test and is constructed by manually written codes, and the benchmark test suite contains definite loopholes which meet the following 4-point conditions: across the execution lifecycle of the program, embedding representative control and data flows, providing input use cases as proof of existence, and listing a small portion of possible input use cases;
in this embodiment, the test benchmark suite selects the LAVA-M standard corpus. The LAVA-M corpus is four copies of coretils version 8.24 source code; one copy has 44 errors injected in base64 and 44 inputs are known to trigger these errors, 57 holes in the other copy md5sum and 28 holes in the third copy uniq, respectively. Finally, there is one copy containing 2136 vulnerabilities that exist at the same time and are expressed separately in who. The LAVA-M adds a mechanism to ensure that each embedded vulnerability is triggered only by a specific input, i.e., if the inputs listed in the LAVA-M are included in the unique crash points, the crash points are all triggered by the corresponding unique vulnerabilities, and the number of crash points represents the unique vulnerability number.
In this embodiment, the number of tests is set to 5;
in this embodiment, the timeout is set to 5 hours,
in this embodiment, the reference blur test tool is AFLFuzz.
In this embodiment, the seeds are constructed manually. The AFLFUzz is used as a reference fuzzy test tool, and because the initial tester case is required to be manually input as an initial seed when the AFLFUzz is used, a plurality of test cases are generated in a mutation-based mode, and the manual construction mode is adopted, the test results of the tested tool and the AFLFUzz tool are comparable.
S2: selecting an evaluation index;
determining according to the input, output and intermediate processes of the fuzzy test tool;
in this embodiment, the evaluation indexes include the number of crash points, code coverage, average time of exposing crash points, the number of vulnerabilities, the number of high-risk vulnerabilities, and the occupancy rate of resources (CPU);
the number of crash points refers to the number of test cases that cause the analysis target to crash in the fuzzy test, and it should be noted that the unique crash points refer to test cases in which the path that causes the analysis target to crash is unique.
Code coverage refers to the measure of the path recognition capability or code fragmentation capability of the ambiguity test tool for the analysis target. Typically, the evaluation index is proportional to the number of unique crash points.
The average time to expose the crash points is the ratio between the total length of time used for the paste test and the number of unique crash points that have been found.
The vulnerability number refers to the classification number of the crashed points belonging to different types of vulnerabilities, and the possibility of the crashed points caused by the vulnerabilities is evaluated. The relationship between the crash points and the loopholes can be a many-to-one relationship, i.e. multiple crash points can be caused by the same loophole. The statistical vulnerability count may be implemented by selecting a particular test benchmark suite or using a vulnerability type analysis tool. A specific test benchmark suite (such as LAVA-M) needs to add a mechanism for ensuring that each embedded vulnerability is triggered by only one specific input, and if a breakdown point output by the fuzzy test tool belongs to the specific input, the breakdown point can be judged to belong to the vulnerability type corresponding to the specific input. In this example, the vulnerability count is based on a test benchmark set, and the vulnerability count refers to the number of marked vulnerabilities in the benchmark test set among vulnerabilities detected by the fuzzy test tool.
The high-risk loopholes are the number of the loopholes detected by the fuzzy test tool, which belong to marked loopholes in the reference test suite and belong to high-risk loopholes. The severity level of each type of embedded vulnerability needs to be preset in the test benchmark suite.
The occupancy rate of the resource (CPU) is in direct proportion to the number of test cases generated by the fuzzy test tool. A plurality of test cases are generated based on a seed variation mode and used for fuzzy test, and under the condition that the number of collapse points is consistent, if the tool finds a certain number of collapse points by using fewer test cases, the seed variation algorithm used by the fuzzy test tool is shown to have advantages.
S3: constructing an evaluation model, and carrying out comprehensive quantitative evaluation on evaluation indexes;
the specific process is as follows:
s301: based on the evaluation indexes, the fuzzy test tool to be tested is tested for each evaluation index to obtain a plurality of values corresponding to each evaluation index, and an evaluation index value matrix X= (X) is constructed ij ) (m×n) M is the number of tool tests; n is the number of tool evaluation indexes, and i and j represent data index values;
s302: carrying out data forward processing on the obtained corresponding values of each evaluation index;
the evaluation indexes of the fuzzy test tool can be divided into two types according to analysis types, wherein the smaller the numerical value is, the better the evaluation result is, such as: average time and resource (CPU) occupancy to expose crash points; another category is that the larger the number, the better the evaluation result, such as: the number of crash points, the path coverage, the number of loopholes and the number of high-risk loopholes.
In this embodiment, in order to simplify data analysis, data is subjected to forward processing so that a larger value indicates a better evaluation result.
The specific calculation formula is as follows:
wherein ,representing forward data, x j Representing a value set corresponding to a certain evaluation index, x ij Indicating that forward data is required.
S303: carrying out standardization processing on the forward data;
because each type of evaluation index value has different dimensions, the dimensions of different evaluation indexes may not be matched, and the comprehensive quantitative evaluation result may be affected, for example, the number of collapse points uses integer counting, the value may adopt 'thousands' as dimensions, but the resource occupancy rate is expressed in percentage, and the value is 0, 100, so that the data needs to be standardized;
however, in practice, there may be a "0" value in the data after the positive processing, which may cause a larger difference between the negative evaluation index weight and the actual observation weight in the square sum normalization method in the classical multi-attribute decision method, and the negative evaluation index weight is generally larger than the positive evaluation index weight value, and does not conform to the actual situation.
In this embodiment, a specific calculation formula of data normalization is as follows:
wherein ,yij In order to normalize the data it is,representing forward data, ">Minimum value in column j representing forward data, +.>Represents the maximum value in the j-th column of the forward data, i takes on the value {1,2,3 …, m }, j takes on the value{1,2,3 …, n }.
S304: calculating the weight of each evaluation index;
in the embodiment, a weighted entropy method is adopted to calculate weights, including entropy calculation, difference coefficient calculation and weight calculation;
the specific process is as follows:
calculating an entropy value:
wherein ,ej For a certain evaluation index entropy value, m represents the number of the corresponding numerical values of the evaluation index, namely the number of matrix rows,representing the proportion of the ith numerical value of a certain evaluation index to the sum of all the numerical values of the evaluation index;
calculating a difference coefficient:
G j ＝1-e j
wherein ,Gj A difference coefficient representing a certain evaluation index;
calculating weights:
wherein ,Wj Representing the weight of a certain evaluation index.
S305: the comprehensive quantization evaluation value is calculated as follows:
calculating a weighted normalization matrix; calculating an optimal solution and a worst solution of each evaluation index according to the weighted standardized matrix; and calculating Euclidean distances of the optimal solution and the worst solution of each evaluation index through the weighted standardized matrix, the optimal solution and the worst solution, and finally calculating and outputting the comprehensive quantized evaluation value of the tested fuzzy test tool through the obtained Euclidean distances.
In this embodiment, the specific calculation process is as follows:
calculating a weighted normalization matrix Z ij ：
Z ij ＝W j ×y ij
Calculating optimal solutions and worst solutions of all evaluation indexes:
BI j + ＝max(z 1j ,z 2j ,...,z mj )
WI j - ＝min(z 1j ,z 2j ,...,z mj )
wherein ,BIj + Represents the optimal solution of each evaluation index, WI j - Representing the worst solution of each evaluation index, z mj For weighting the values in the normalization matrix, m takes the value {1,2, …, m }.
Calculating the Euclidean distance between the optimal solution and the worst solution of each evaluation index:
wherein ,Di + Represents the Euclidean distance of each evaluation index optimal solution, D i - Representing the Euclidean distance, z of the worst solution of each evaluation index ij Values in the matrix are normalized for weighting.
Calculating a comprehensive quantization evaluation value:
c corresponding to the fuzzy test tool to be tested i The larger the value, the higher the overall quantization score for the fuzzy test tool.
The invention is not limited to the specific embodiments described above. The invention extends to any novel one, or any novel combination, of the features disclosed in this specification, as well as to any novel one, or any novel combination, of the steps of the method or process disclosed.
Claims (5)
1. The comprehensive quantitative evaluation method for the performance of the fuzzy test tool is characterized by comprising the following steps of:
s1: selecting a test benchmark suite and setting experimental conditions;
the test benchmark suite includes vulnerabilities that satisfy the conditions: across the execution lifecycle of the program, embedding representative control and data flows, providing input use cases as proof of existence, and listing the input use cases;
the experimental conditions comprise test times, overtime time, a reference fuzzy test tool and seeds;
s2: selecting an evaluation index;
the evaluation indexes comprise the number of collapse points, code coverage rate, average time for exposing the collapse points, the number of loopholes, the number of high-risk loopholes and resource occupancy rate;
s3: constructing an evaluation model, and carrying out comprehensive quantitative evaluation on evaluation indexes, wherein the process is as follows:
s301: constructing an evaluation index numerical matrix;
by testing the fuzzy test tool to be evaluated for a plurality of times under the experimental conditions aiming at each evaluation index, the evaluation index value corresponding to each evaluation index is obtained, and the evaluation index value matrix is constructed, wherein the evaluation index value matrix is as follows:
X＝(x ij ) (m×n )
wherein m represents the number of tests, and n represents the number of evaluation indexes;
s302: carrying out data forward processing on the obtained corresponding values of each evaluation index, and specifically calculating as follows:
wherein ,representing forward data, x j Representing a value set corresponding to a certain evaluation index, x ij Data representing that forward localization is required;
s303: the normalized data is normalized, and is specifically calculated as follows:
wherein ,yij In order to normalize the data it is,minimum value in column j representing forward data, +.>The maximum value in the j-th column of the forward data is represented, i takes the value {1,2,3 …, m }, and j takes the value {1,2,3 …, n };
s304: calculating the weight of each evaluation index;
s305: and (5) comprehensive quantitative evaluation index calculation.
2. The comprehensive quantitative evaluation method for performance of a fuzzy test tool according to claim 1, wherein in step S304, the weight of the evaluation index includes calculating an entropy value of the evaluation index, calculating a difference coefficient, and calculating a weight, and the specific procedures are as follows:
the evaluation index entropy value is calculated as follows:
wherein ,ej Represents the entropy value of the evaluation index, m represents the number of the numerical values corresponding to the evaluation index,representing the i-th number value of the evaluation index to be taken as the evaluation indexThe specific gravity of the sum of all values;
the difference coefficient is calculated as follows:
G j ＝1-e j
wherein ,Gj A difference coefficient representing an evaluation index;
the weight is calculated as follows:
wherein ,Wj And represents the weight of the evaluation index.
3. The comprehensive quantitative evaluation method for performance of a fuzzy test tool according to claim 1, wherein in step S305, the comprehensive quantitative evaluation index calculation includes: calculating a weighted normalization matrix through evaluating index weights; calculating an optimal solution and a worst solution of each evaluation index according to the weighted standardized matrix; and calculating Euclidean distances of the optimal solution and the worst solution of each evaluation index through the weighted standardized matrix, the optimal solution and the worst solution, and finally calculating and outputting the comprehensive quantized evaluation value of the tested fuzzy test tool through the obtained Euclidean distances.
4. A comprehensive quantitative evaluation method for performance of a fuzzy test tool according to any one of claims 1 to 3, wherein the test benchmark suite is a LAVA-M standard corpus, the number of tests is 5, and the timeout period is 5h.
5. A comprehensive quantitative assessment method for performance of a fuzzy test tool according to any one of claims 1 to 3, wherein said reference fuzzy test tool is AFLFuzz.
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