Data Mining - Concepts and Techniques.

Software birthmark is an important property of software that is successfully used to detect piracy and theft of software. Estimation of a birthmark provides critical information about the extent of piracy performed in a software. This information can then be used to decide over many important issues related with software theft and piracy, including legal and ethical considerations. Research shows that a software birthmark based on multiple software features provides a powerful and unique identity to software, hence more useful in theft detection. The estimation of this features based birthmark may provide even close detection and estimation of software piracy. This estimation process provides an objective measure to detect software theft and piracy efficiently and accurately. The research uses the concept of fuzzy logic for estimation, which has already proved its success in estimation of other birthmarks. The technique is tested for features based birthmark through a case study and the results support validity of the process. The details of the case study elaborate upon techniques and details of implementing software birthmark estimation process and show that the method is effective in terms of efficiency and accuracy for the estimation of features based software birthmark.

Estimation of software features based birthmark

The result of feature selection for software birthmark has a direct bearing on software recognition rate. In this paper, we apply constrained clustering to analyze the software features (SF). The within-class (homogeneous software) and between-class (heterogeneous software) distances of features are measured based on mutual information. Information gain functions and penalty functions are constructed using homogeneous and heterogeneous SF, respectively; and redundancy is measured with correlation coefficients. Then the software birthmark features with high class distinction and minimum redundancy are selected. The example of extracting and detecting framework of birthmark feature is also given. The algorithm is analyzed and compared with the similar algorithms, and it is shown the algorithm provide an effective approach for software birthmark selection and optimization.

Statistics and recognition for software birthmark based on clustering analysis

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Statistics and recognition for software birthmark based on clustering analysis