Android system privacy leakage detection-oriented dynamic characteristic information extraction method

Abstract

The invention discloses an android system privacy leakage detection-oriented dynamic characteristic information extraction method. The method comprises the following steps of: extracting dynamic characteristics to call dynamic characteristic calling subgraphs on which reverse extraction depends on the basis of calling graphs between and in components, so as to decrease unnecessary code analysis and limit the analysis in relatively small program codes; dividing dynamic characteristic calling paths for the calling subgraphs according to different definition-using relationships of target parameters, so as to obtain more trigger calling paths; carrying out parameter constraint analysis on the basis of slice information of each calling path, and combining the parameters to generate test cases so as to construct more test cases; and inputting the test cases, and instructing dynamic test to obtain dynamic characteristic information so as to avoid random test. According to the method, the dynamic test takes static analysis as instruction, so that path sensitivity and context sensitivity are ensured, the coverage rate and accuracy of dynamic characteristic information obtaining are improved, the randomness of the dynamic characteristic information obtaining is avoided, and the coverage and obtaining efficiency of the dynamic characteristic information are improved.

Description

technical field [0001] The invention belongs to the field of dynamic characteristic information extraction in codes, and in particular relates to a method for extracting dynamic characteristic information oriented to Android system privacy leakage detection. Background technique [0002] In privacy information leakage detection, taint analysis is usually based on data flow, but the dynamic characteristics in the code will form breakpoints in data flow, hindering the analysis of taint propagation. In order to make up for data flow breakpoints, dynamic testing is usually used to obtain breakpoint information to make up for dynamic characteristic breakpoints in data flow analysis. However, most of the current testing methods use dynamic random testing, without in-depth analysis of the triggering factors of dynamic characteristics, and the coverage is incomplete, resulting in the omission of dynamic characteristic information and incomplete analysis of private information leakag...

AI Technical Summary

Problems solved by technology

However, most of the current test methods use dynamic random testing, without in-depth analysis of the trigger factors of dynamic characteristics, and the coverage is incomplete, resulting in the omission of dynamic characteristic information and incomplete analysis of privacy information leakage.

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