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Sentiment analysis in a forensic timeline with deep learning
Journal article   Peer reviewed

Sentiment analysis in a forensic timeline with deep learning

H. Studiawan, F. Sohel and C. Payne
IEEE Access, Vol.8, pp.60664-60675
2020
DOI: https://doi.org/10.1109/ACCESS.2020.2983435
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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.

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86 Record Views
23 Times Cited - Web of Science

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.187 Security Systems
4.187.1404 Malware Detection
Web Of Science research areas
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
ESI research areas
Engineering
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