With the widespread use of agile software development methods,such as agile and scrum,software is iteratively updated more frequently.To ensure the quality of the software,regression testing is conducted before new ve...With the widespread use of agile software development methods,such as agile and scrum,software is iteratively updated more frequently.To ensure the quality of the software,regression testing is conducted before new versions are released.Moreover,to improve the efficiency of regression testing,testing efforts should be concentrated on the modified and impacted parts of a program.However,the costs of manually constructing new test cases for the modified and impacted parts are relatively expensive.Fuzz testing is an effective method for generating test data automatically,but it is usually devoted to achieving higher code coverage,which makes fuzz testing unsuitable for direct regression testing scenarios.For this reason,we propose a fuzz testing method based on the guidance of historical version information.First,the differences between the program being tested and the last version are analyzed,and the results of the analysis are used to locate change points.Second,change impact analysis is performed to find the corresponding impacted basic blocks.Finally,the fitness values of test cases are calculated according to the execution traces,and new test cases are generated iteratively by the genetic algorithm.Based on the proposed method,we implement a prototype tool DeltaFuzz and conduct experiments on six open-source projects.Compared with the fuzzing tool AFLGo,AFLFast and AFL,DeltaFuzz can reach the target faster,and the time taken by DeltaFuzz was reduced by 20.59%,30.05%and 32.61%,respectively.展开更多
By the analysis of vulnerabilities of Android native system services,we find that some vulnerabilities are caused by inconsistent data transmission and inconsistent data processing logic between client and server.The ...By the analysis of vulnerabilities of Android native system services,we find that some vulnerabilities are caused by inconsistent data transmission and inconsistent data processing logic between client and server.The existing research cannot find the above two types of vulnerabilities and the test cases of them face the problem of low coverage.In this paper,we propose an extraction method of test cases based on the native system services of the client and design a case construction method that supports multi-parameter mutation based on genetic algorithm and priority strategy.Based on the above method,we implement a detection tool-BArcherFuzzer to detect vulnerabilities of Android native system services.The experiment results show that BArcherFuzzer found four vulnerabilities of hundreds of exception messages,all of them were confirmed by Google and one was assigned a Common Vulnerabilities and Exposures(CVE)number(CVE-2020-0363).展开更多
Network protocols are divided into stateless and stateful. Stateful network protocols have complex communication interactions and state transitions. However, the existing network protocol fuzzing does not support stat...Network protocols are divided into stateless and stateful. Stateful network protocols have complex communication interactions and state transitions. However, the existing network protocol fuzzing does not support state transitions very well. This paper focuses on this issue and proposes the Semi-valid Fuzzing for the Stateful Network Protocol （SFSNP）. The SFSNP analyzes protocol interactions and builds an extended finite state machine with a path marker for the network protocol; then it obtains test sequences of the extended finite state machine, and further performs the mutation operation using the semi-valid algorithm for each state transition in the test sequences; finally, it obtains fuzzing sequences. Moreover, because different test sequences may have the same state transitions, the SFSNP uses the state transition marking algorithm to reduce redundant test cases. By using the stateful rule tree of the protocol, the SFSNP extracts the constraints in the protocol specifications to construct semi-valid fuzz testing cases within the sub-protocol domain, and finally forms fuzzing sequences. Experimental results indicate that the SFSNP is reasonably effective at reducing the quantity of generated test cases and improving the quality of fuzz testing cases. The SFSNP can reduce redundancy and shorten testing time.展开更多
基金supported by the Leading-Edge Technology Program of Jiangsu Natural Science Foundation of China under Grant No.BK20202001the National Natural Science Foundation of China under Grant No.61702041the Beijing Information Science and Technology University“Qin-Xin Talent”Cultivation Project under Grant No.QXTCP C201906.
文摘With the widespread use of agile software development methods,such as agile and scrum,software is iteratively updated more frequently.To ensure the quality of the software,regression testing is conducted before new versions are released.Moreover,to improve the efficiency of regression testing,testing efforts should be concentrated on the modified and impacted parts of a program.However,the costs of manually constructing new test cases for the modified and impacted parts are relatively expensive.Fuzz testing is an effective method for generating test data automatically,but it is usually devoted to achieving higher code coverage,which makes fuzz testing unsuitable for direct regression testing scenarios.For this reason,we propose a fuzz testing method based on the guidance of historical version information.First,the differences between the program being tested and the last version are analyzed,and the results of the analysis are used to locate change points.Second,change impact analysis is performed to find the corresponding impacted basic blocks.Finally,the fitness values of test cases are calculated according to the execution traces,and new test cases are generated iteratively by the genetic algorithm.Based on the proposed method,we implement a prototype tool DeltaFuzz and conduct experiments on six open-source projects.Compared with the fuzzing tool AFLGo,AFLFast and AFL,DeltaFuzz can reach the target faster,and the time taken by DeltaFuzz was reduced by 20.59%,30.05%and 32.61%,respectively.
基金This work was supported by the National Key R&D Program of China(2023YFB3106800)the National Natural Science Foundation of China(Grant No.62072051).We are overwhelmed in all humbleness and gratefulness to acknowledge my depth to all those who have helped me to put these ideas.
文摘By the analysis of vulnerabilities of Android native system services,we find that some vulnerabilities are caused by inconsistent data transmission and inconsistent data processing logic between client and server.The existing research cannot find the above two types of vulnerabilities and the test cases of them face the problem of low coverage.In this paper,we propose an extraction method of test cases based on the native system services of the client and design a case construction method that supports multi-parameter mutation based on genetic algorithm and priority strategy.Based on the above method,we implement a detection tool-BArcherFuzzer to detect vulnerabilities of Android native system services.The experiment results show that BArcherFuzzer found four vulnerabilities of hundreds of exception messages,all of them were confirmed by Google and one was assigned a Common Vulnerabilities and Exposures(CVE)number(CVE-2020-0363).
基金supported by the National Key R&D Program of China(No.2016YFB0800700)
文摘Network protocols are divided into stateless and stateful. Stateful network protocols have complex communication interactions and state transitions. However, the existing network protocol fuzzing does not support state transitions very well. This paper focuses on this issue and proposes the Semi-valid Fuzzing for the Stateful Network Protocol （SFSNP）. The SFSNP analyzes protocol interactions and builds an extended finite state machine with a path marker for the network protocol; then it obtains test sequences of the extended finite state machine, and further performs the mutation operation using the semi-valid algorithm for each state transition in the test sequences; finally, it obtains fuzzing sequences. Moreover, because different test sequences may have the same state transitions, the SFSNP uses the state transition marking algorithm to reduce redundant test cases. By using the stateful rule tree of the protocol, the SFSNP extracts the constraints in the protocol specifications to construct semi-valid fuzz testing cases within the sub-protocol domain, and finally forms fuzzing sequences. Experimental results indicate that the SFSNP is reasonably effective at reducing the quantity of generated test cases and improving the quality of fuzz testing cases. The SFSNP can reduce redundancy and shorten testing time.
