A Framework for Post-Event Timeline Reconstruction using Neural Networks

Khan, M N A, Chatwin, C R and Young, R C D (2007) A Framework for Post-Event Timeline Reconstruction using Neural Networks. Digital Investigation, 4. pp. 146-157. ISSN 1742-2876

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Post-event timeline reconstruction plays a critical role in forensic investigation and serves as a means of identifying evidence of the digital crime. We present an artificial neural networks based approach for post-event timeline reconstruction using the file system activities. A variety of digital forensic tools have been developed during the past two decades to assist computer forensic investigators undertaking digital timeline analysis, but most of the tools cannot handle large volumes of data efficiently. This paper looks at the effectiveness of employing neural network methodology for computer forensic analysis by preparing a timeline of relevant events occurring on a computing machine by tracing the previous file system activities. Our approach consists of monitoring the file system manipulations, capturing file system snapshots at discrete intervals of time to characterise the use of different software applications, and then using this captured data to train a neural network to recognise execution patterns of the application programs. The trained version of the network may then be used to generate a post-event timeline of a seized hard disk to verify the execution of different applications at different time intervals to assist in the identification of available evidence.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Depositing User: Muhammad Naeem Ahmad Khan
Date Deposited: 06 Feb 2012 21:17
Last Modified: 03 Apr 2012 08:34
URI: http://sro.sussex.ac.uk/id/eprint/30646
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