As software is getting more valuable, unauthorized users or malicious
programmers illegally copies and distributes copyrighted software over
online service provider (OSP) and P2P networks. To detect, block, and remove
pirated software (illegal programs) on OSP and P2P networks, this paper
proposes a new filtering approach using software birthmark, which is unique
characteristics of program and can be used to identify each program.
Software birthmark typically includes constant values, library information,
sequence of function calls, and call graphs, etc. We target Microsoft
Windows applications and utilize the numbers and names of DLLs and APIs
stored in a Windows executable file. Using that information and each
cryptographic hash value of the API sequence of programs, we construct
software birthmark database. Whenever a program is uploaded or downloaded on
OSP and P2P networks, we can identify the program by comparing software
birthmark of the program with birthmarks in the database. It is possible to
grasp to some extent whether software is an illegally copied one. The
experiments show that the proposed software birthmark can effectively
identify Windows applications. That is, our proposed technique can be
employed to efficiently detect and block pirated programs on OSP and P2P
networks.
As software industry has been grown, it occurs more frequently to illegally copy software or to steal the core modules of a program. In order to detect program plagiarism, similarity analysis of suspicious programs based on source codes is one of accurate methods. However, the source codes are not always available. Therefore, it is necessary to analyze and determine software piracy or theft with only binary executables that are release versions of their products. In this paper, we propose a method to extract the feature information from the binary codes of the executable files on MS Windows systems in order to determine whether software is pirated or core modules of a program are stolen. We perform a small experiment to detect program similarity and plagiarism by comparing the statically extracted features of target programs.
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