A Decision-Driven Computer Forensic Classification Using ID3 Algorithm

Rapid evolution of information technology has caused devices to be used in criminal activities. Criminals have been using the Internet to distribute a wide range of illegal materials globally, making tracing difficult for the purpose of initiating digital investigation process. Forensic digital analysis is unique and inherently mathematical and generally comprises more data from an investigation than is present in other types of forensic investigations. To provide appropriate and sufficient security measures has become a difficult job due to large volume of data and complexity of the devices making the investigation of digital crimes even harder. Data mining and data fusion techniques have been used as useful tools for detecting digital crimes. In this study, we have introduced a forensic classification problem and applied ID3 decision tree learning algorithm for supervised exploration of the forensic data which will also enable visualization and will reduce the complexity involved in digital investigation process.