Document Type Master's Dissertation Author Fei, Bennie Kar Leung URN etd-03072007-153241 Document Title Data visualisation in digital forensics Degree MSc (Computer Science) Department Computer Science Supervisor
Advisor Name Title Dr H S Venter Co-Supervisor Prof M S Olivier Co-Supervisor Prof J H P Eloff Supervisor Keywords
- digital forensics
- digital investigation
- self-organising map
Date 2007-02-01 Availability unrestricted AbstractAs digital crimes have risen, so has the need for digital forensics. Numerous state-of-the-art tools have been developed to assist digital investigators conduct proper investigations into digital crimes. However, digital investigations are becoming increasingly complex and time consuming due to the amount of data involved, and digital investigators can find themselves unable to conduct them in an appropriately efficient and effective manner. This situation has prompted the need for new tools capable of handling such large, complex investigations. Data mining is one such potential tool. It is still relatively unexplored from a digital forensics perspective, but the purpose of data mining is to discover new knowledge from data where the dimensionality, complexity or volume of data is prohibitively large for manual analysis.
This study assesses the self-organising map (SOM), a neural network model and data mining technique that could potentially offer tremendous benefits to digital forensics. The focus of this study is to demonstrate how the SOM can help digital investigators to make better decisions and conduct the forensic analysis process more efficiently and effectively during a digital investigation. The SOMís visualisation capabilities can not only be used to reveal interesting patterns, but can also serve as a platform for further, interactive analysis.
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