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A cyber-threat analytic model for autonomous detection of virtual property theft

A cyber-threat analytic model for autonomous detection of virtual property theft PurposeThe purpose of this study is to provide a framework to detect and prevent virtual property theft in virtual world environments. The issue of virtual property theft is a serious problem which has ramifications in both the real and virtual world. Virtual world users invest a considerable amount of time, effort and often money to collect virtual property, only to have them stolen by thieves. Many virtual property thefts go undetected and often only discovered after the incident has occurred.Design/methodology/approachThis paper presents the design of an autonomic detection framework to identify virtual property theft at two key stages: account intrusion and virtual property trades. Account intrusion is an unauthorized user attempting to gain access to an account and unauthorized virtual property trades are trading of items between two users which exhibit theft characteristics.FindingsInitial tests of this framework on a synthetic data set show an 80 per cent detection rate. This framework allows virtual world developers to tailor and extend it to suit their specific requirements. It provides an effective way of detecting virtual property theft while being low maintenance, user friendly and cost effective.Originality/valueTo the author’s knowledge, there is no detection framework, system or tool that works on virtual property theft detection in virtual world environments without access to authentic virtual world data or attack data (because of privacy issues and unwillingness of virtual world environments companies to collaborate). The topic of virtual property theft, lack of existing labelled data sets, user anonymity, size of virtual world environments data sets and privacy issues with virtual world companies and a number of other critical factors distinguish this paper from previous studies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Information & Computer Security Emerald Publishing

A cyber-threat analytic model for autonomous detection of virtual property theft

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2056-4961
DOI
10.1108/ICS-11-2016-0087
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this study is to provide a framework to detect and prevent virtual property theft in virtual world environments. The issue of virtual property theft is a serious problem which has ramifications in both the real and virtual world. Virtual world users invest a considerable amount of time, effort and often money to collect virtual property, only to have them stolen by thieves. Many virtual property thefts go undetected and often only discovered after the incident has occurred.Design/methodology/approachThis paper presents the design of an autonomic detection framework to identify virtual property theft at two key stages: account intrusion and virtual property trades. Account intrusion is an unauthorized user attempting to gain access to an account and unauthorized virtual property trades are trading of items between two users which exhibit theft characteristics.FindingsInitial tests of this framework on a synthetic data set show an 80 per cent detection rate. This framework allows virtual world developers to tailor and extend it to suit their specific requirements. It provides an effective way of detecting virtual property theft while being low maintenance, user friendly and cost effective.Originality/valueTo the author’s knowledge, there is no detection framework, system or tool that works on virtual property theft detection in virtual world environments without access to authentic virtual world data or attack data (because of privacy issues and unwillingness of virtual world environments companies to collaborate). The topic of virtual property theft, lack of existing labelled data sets, user anonymity, size of virtual world environments data sets and privacy issues with virtual world companies and a number of other critical factors distinguish this paper from previous studies.

Journal

Information & Computer SecurityEmerald Publishing

Published: Oct 9, 2017

References