An Improved Data Mining Tools Classifier for Digital Forerisic Analysis

Abstract

Due to prevalent issues of crime, enhanced by modern digital technologies, it has become necessary to find better ways of improving the methods used in digital investigations. Digital forensics addresses the specific need to be able to extract legally admissible evidence from computer systems, networks and other computing devices that can be used to successfully prosecute digital criminals. This process has become more time consuming and complex as the volumes of data requiring analysis continue to grow. In this study, an evaluation is made of data mining algorithms that can best establish the relevance of a digital device to a criminal case without the need for in-depth forensic examination. Thus, an improvement was affected on classification accuracies of two different data mining algorithms, namely K-Nearest Neighbours and Neural Networks. This was achieved by mitigating the effects of imbalance and overfitting in the dataset and obtained results compared with a specified benchmark. The results show that when the pre-processing data techniques, of synthetic oversampling, training exemplars randomization, and better feature selection techniques were applied, the two selected algorithms 
outperformed some others.