Computer Crime Forensics Based on Improved Decision Tree Algorithm

Abstract

To find out the evidence of crime-related evidence and association rules among massive data, the classic decision tree algorithms such as ID3 for classification analysis have appeared in related prototype systems. So how to make it more suitable for computer forensics in variable environments becomes a hot issue. When selecting classification attributes, ID3 relies on computation of information entropy. Then the attributes owning more value are selected as classification nodes of the decision tress. Such classification is unrealistic under many cases. During the process of ID3 algorithm there are too many logarithms, so it is complicated to handle with the dataset which has various classification attributes. Therefore, contraposing the special demand for computer crime forensics, ID3 algorithm is improved and a novel classification attribute selection method based on Maclaurin-Priority Value First method is proposed. It adopts the foot changing formula and infinitesimal substitution to simplify the logarithms in ID3. For the errors generated in this process, an apposite constant is introduced to be multiplied by the simplified formulas for compensation. The idea of Priority Value First is introduced to solve the problems of value deviation. The performance of improved method is strictly proved in theory. Finally, the experiments verify that our scheme has advantage in computation time and classification accuracy, compared to ID3 and two existing algorithms