Description: DETECTING SOURCE CODE RE-USE THROUGH METADATA AND CONTEXT PARTIAL HASHING
Often, digital investigations have to deal with the theft of intellectual property in the form of source code copied and re-used without permission. In order to investigate source code infringement, there are two main family techniques: plaintext source code comparison and binary analysis. Both kinds of analysis can give very accurate results when comparing identical or very similar datasets. Nevertheless, they fail very easily in case any kind of source code obfuscation, data hiding or simple scrambling techniques are applied to one of the compared datasets. In this paper, we present a novel hybrid approach, merging both plain-text, binary and cryptographic comparison analysis techniques by taking advantage of several common features shared across the most common computer binary executable formats.Often, digital investigations have to deal with the theft of intellectualproperty in the form of source code copied and re-used without permission.In order to investigate source code infringement, there are two mainfamily techniques: plaintext source code comparison and binary analysis.Both kinds of analysis can give very accurate results when comparingidentical or very similar datasets. Nevertheless, they fail very easily incase any kind of source code obfuscation, data hiding or simple scramblingtechniques are applied to one of the compared datasets.In this paper, we present a novel hybrid approach, merging bothplain-text, binary and cryptographic comparison analysis techniques bytaking advantage of several common features shared across the most commoncomputer binary executable formats.
Often, digital investigations have to deal with the theft of intellectual property in the form of source code copied and re-used without permission. In order to investigate source code infringement, there are two main family techniques: plaintext source code comparison and binary analysis. Both kinds of analysis can give very accurate results when comparing identical or very similar datasets. Nevertheless, they fail very easily in case any kind of source code obfuscation, data hiding or simple scrambling techniques are applied to one of the compared datasets. In this paper, we present a novel hybrid approach, merging both plain-text, binary and cryptographic comparison analysis techniques by taking advantage of several common features shared across the most common computer binary executable formats.
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