Computer forensic classification with ID3 algorithm

3^rd International Conference on Forensic Research and Technology

October 06-08, 2014 Hilton San Antonio Airport, USA

Suneeta Satpathy, Sateesh K Pradhan and B B Ray

Accepted Abstracts: J Forensic Res

Abstract :

Rapid evolution of Information Technology has caused devices to be used in criminal activities. Criminals in the digital age have been using the Internet to distribute a wide range of illegal materials globally, making criminal activity tracing difficult in the 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 forensics investigation. 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 has been used as an useful tool for detecting and preventing such types of digital crimes. In this study we introduced a forensic classification problem and applied ID3 decision tree learning data mining algorithm to automatically explore the forensic data and trace the digital criminals. It can enable visualization and can reduce the complexity involved in the analysis of large volume of data in digital investigation process.

Biography :

Suneeta Satpathy, working as Assistant Professor in Department of Computer Science & Engineering, College of Engineering Bhubaneswar, BPUT, Bhubaneswar. She has recently completed her PhD from Utkal University in the area of Computer Forensics in Dec 2013. She was also a Research Fellow in Computer Forensic division, Govt. Examiner of Questioned Documents, Central Forensic Science Laboratory, Directorate of Forensic Sciences, Hyderabad from Dec 2002 to Dec 2005.