Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Sentiment analysis in a forensic timeline with deep learning

Studiawan, H., Sohel, F. and Payne, C. (2020) Sentiment analysis in a forensic timeline with deep learning. IEEE Access, 8 . pp. 60664-60675.

Link to Published Version: https://doi.org/10.1109/ACCESS.2020.2983435
*Subscription may be required

Abstract

A forensic investigator creates a timeline from a forensic disk image after an occurrence of a security incident. This procedure aims to acquire the time for all events identified from the investigated artifacts. An investigator usually looks for events of interest by manually searching the timeline. One of the sources from which to build a timeline is log files, and these events are often found in log messages. In this paper, we propose a sentiment analysis technique to automatically extract events of interest from log messages in the forensic timeline. We use a deep learning technique with a context and content attention model to identify aspect terms and the corresponding sentiments in the forensic timeline. Terms with negative sentiments indicate events of interest and are highlighted in the timeline. Therefore, the investigator can quickly examine the events and other activities recorded within the surrounding time frame. Experimental results on four public forensic case studies show that the proposed method achieves 98.43% and 99.64% for the F1 score and accuracy, respectively.

Item Type: Journal Article
Murdoch Affiliation: College of Science, Health, Engineering and Education
Publisher: IEEE
Copyright: © 2020 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/55718
Item Control Page Item Control Page