Automatic detection of potentially illegal online sales of elephant ivory via data mining

Hernandez-Castro, Julio C., Roberts, David L. (2015) Automatic detection of potentially illegal online sales of elephant ivory via data mining. PeerJ Computer Science, 1:e10 . pp. 1-11. ISSN 2376-5992. (doi:10.7717/peerj-cs.10)

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Abstract

In this work, we developed an automated system to detect potentially illegal elephant ivory items for sale on eBay. Two law enforcement experts, with specific knowledge of elephant ivory identification, manually classified items on sale in the Antiques section of eBay UK over an 8 week period. This set the “Gold Standard” that we aim to emulate using data-mining. We achieved close to 93% accuracy with less data than the experts, as we relied entirely on metadata, but did not employ item descriptions or associated images, thus proving the potential and generality of our approach. The reported accuracy may be improved with the addition of text mining techniques for the analysis of the item description, and by applying image classification for the detection of Schreger lines, indicative of elephant ivory. However, any solution relying on images or text description could not be employed on other wildlife illegal markets where pictures can be missing or misleading and text absent (e.g., Instagram). In our setting, we gave human experts all available information while only using minimal information for our analysis. Despite this, we succeeded at achieving a very high accuracy. This work is an important first step in speeding up the laborious, tedious and expensive task of expert discovery of illegal trade over the internet. It will also allow for faster reporting to law enforcement and better accountability. We hope this will also contribute to reducing poaching, by making this illegal trade harder and riskier for those involved.

Item Type: Article
DOI/Identification number: 10.7717/peerj-cs.10
Subjects: H Social Sciences > HF Commerce > HF5548.32 E-commerce
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Q Science > QH Natural history > QH75 Conservation (Biology)
Q Science > QL Zoology
Divisions: Faculties > University wide - Teaching/Research Groups > Centre for Cyber Security Research
Faculties > Sciences > School of Computing > Security Group
Faculties > Social Sciences > School of Anthropology and Conservation
Faculties > Social Sciences > School of Anthropology and Conservation > Biodiversity Conservation Group
Faculties > Social Sciences > School of Anthropology and Conservation > Biodiversity Management Group
Faculties > Social Sciences > School of Anthropology and Conservation > DICE (Durrell Institute of Conservation and Ecology)
Depositing User: David Roberts
Date Deposited: 29 Jul 2015 14:47 UTC
Last Modified: 29 May 2019 14:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/49885 (The current URI for this page, for reference purposes)
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