Zero-Shot Entity Recognition on Forensic Timeline

Resky Ayu Dewi Talasari, Karina Fitriwulandari Ilham, Hudan Studiawan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Forensic timeline contains standardized entities such as date, time, and host which are essential in a forensic investigation setting. These components need to be analyzed to assist an investigator in analyzing forensic evidence and artifacts. However, traditional entity recognition models often require extensive labeled data for each entity of interest. This becomes challenging in forensic scenarios where new and unseen entities constantly emerge, and labeled data for those entities is non-existent. This paper introduces a method for entity recognition in forensic timeline using zero-shot learning (ZSL) technique by employing the widely used large language models (LLMs), such as ChatGPT and Claude. In this paper, three publicly available different types of datasets downloaded from Digital Corpora namely, 2010-nps-email, nps-2009-casper-rw, and nps-2009-canon-rw, are used to test the proposed approach. Experimental results show that Claude's ZSL model is more consistent than ChatGPT in recognizing entities based on finetuned prompts.

Original languageEnglish
Title of host publication2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages117-122
Number of pages6
ISBN (Electronic)9798350363104
DOIs
Publication statusPublished - 2024
Event10th International Conference on Smart Computing and Communication, ICSCC 2024 - Bali, Indonesia
Duration: 25 Jul 202427 Jul 2024

Publication series

Name2024 10th International Conference on Smart Computing and Communication, ICSCC 2024

Conference

Conference10th International Conference on Smart Computing and Communication, ICSCC 2024
Country/TerritoryIndonesia
CityBali
Period25/07/2427/07/24

Keywords

  • digital forensic
  • forensic timeline
  • log2timeline plaso
  • named entity recognition
  • zero-shot learning

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