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• W2559619994 abstract "Characterizing program behavior using static analysis is a challenging problem. In this work, we focus on the fundamental problem of program similarity quantification, i.e., estimating the behavioral similarity of two programs. The solution to this problem is a sub-routine for many important practical problems, such as malware classification, code-cloning detection, program testing, and so on. The main difficulty is to be able to characterize the run-time program behavior without actually executing the program or performing emulation. In this work, we propose a novel behavior tracing approach to characterize program behaviors. We use the call-dependency relationship among the program API calls to generate a trace of the API calling sequence. The dependency tracking is done in a backward fashion, so as to capture the cause and effect relationship among the API calls. Our hypothesis is that this relationship can capture the program behavior to a large extent. We performed experiments by considering several “versions” of a given software, where each version was generated using the code obfuscation techniques. Our approach was found to be resilient up to 20 % obfuscation, i.e., our approach correctly detected that all obfuscated programs that are similar in behavior based on the API call sequences." @default.
• W2559619994 created "2016-12-08" @default.
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• W2559619994 date "2016-12-01" @default.
• W2559619994 modified "2023-09-23" @default.
• W2559619994 title "Static Program Behavior Tracing for Program Similarity Quantification" @default.
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• W2559619994 doi "https://doi.org/10.1007/978-981-10-2471-9_31" @default.
• W2559619994 hasPublicationYear "2016" @default.
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