Evaluation of Evidence in Internet Auction Fraud Investigations

Kwan, Mei Po; Overill, Richard E.; Chow, Kam-Pui; Silomon, Jantje A. M.; Tse, Hayson; Law, Frank Y. W.; Lai, Pierre K. Y.

doi:10.1007/978-3-642-15506-2_9

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…Since the actual BT BBN used in this study is typical in form and structure of the BBNs employed to investigate other digital crime hypotheses (for example, the Internet auction fraud investigations [4]), this study offers reassurance that those BBNs will also exhibit similarly stable behaviour towards the non-recovery of evidential traces and the values chosen for the evidential likelihoods.…”

Section: Discussionmentioning

confidence: 92%

Sensitivity Analysis of a Bayesian Network for Reasoning about Digital Forensic Evidence

Overill

Silomon

Kwan

et al. 2010

2010 3rd International Conference on Human-Centric Computing

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Bayesian network representing an actual prosecuted case of illegal file sharing over a peer-to-peer network has been subjected to a systematic and rigorous sensitivity analysis. Our results demonstrate that such networks are usefully insensitive both to the occurrence of missing evidential traces and to the choice of conditional evidential probabilities. The importance of this finding for the investigation of digital forensic hypotheses is highlighted.

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Section: Discussionmentioning

confidence: 92%

Sensitivity Analysis of a Bayesian Network for Reasoning about Digital Forensic Evidence

Overill

Silomon

Kwan

et al. 2010

2010 3rd International Conference on Human-Centric Computing

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“…From 20 typical cases of internet auction fraud prosecuted in Hong Kong, China, Bayesian networks for both the prosecution and the defence cases were created and the LR of these two alternative explanations for the existence of the recovered digital evidence was computed to be 164 000 in favour of the prosecution hypothesis [23]. This finding may be interpreted as providing "very strong support" for the prosecution's hypothesis [24].…”

Section: Proposition Plausibility Metricsmentioning

confidence: 99%

Quantitative evaluation of the results of digital forensic investigations: a review of progress

Overill

Collie

2021

Forensic Sciences Research

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Unlike conventional forensics, digital forensics does not at present generally quantify the results of its investigations. It is suggested that digital forensics should aim to catch-up with other forensic disciplines by using Bayesian and other numerical methodologies to quantify its investigations' results. Assessing the plausibility of alternative hypotheses (or propositions, or claims) which explain how recovered digital evidence came to exist on a device could assist both the prosecution and the defence sides in criminal proceedings: helping the prosecution to decide whether to proceed to trial and helping defence lawyers to advise a defendant how to plead. This paper reviews some numerical approaches to the goal of quantifying the relative weights of individual items of digital evidence and the plausibility of hypotheses based on that evidence. The potential advantages enabling the construction of cost-effective digital forensic triage schemas are also outlined. KEY POINTS The absence of quantified results from digital forensic investigations, unlike those of conventional forensics, is highlighted. A number of approaches towards quantitative evaluation of the results of digital forensic investigations are reviewed. The significant potential benefits accruing from such approaches are discussed.

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“…Forty-four attributes are defined and analyzed in this paper with the aim to build a model for early detection of fraud. Kwan et al [40] focus on the detection of selling fake products. They define attributes of this fraudulent behavior and use the Bayes approach in their evaluation.…”

Section: Related Workmentioning

confidence: 99%

Uncertain Reasoning for Detection of Selling Stolen Goods in Online Auctions Using Contextual Information

Beránek

2014

Advances in Decision Sciences

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This work describes the design of a decision support system for detection of fraudulent behavior of selling stolen goods in online auctions. In this system, each seller is associated with a type of certification, namely “proper seller,” “suspect seller,” and “selling stolen goods.” The certification level is determined on the basis of a seller’s behaviors and especially on the basis of contextual information whose origin is outside online auctions portals. In this paper, we focus on representing knowledge about sellers in online auctions, the influence of additional information available from other Internet source, and reasoning on bidders’ trustworthiness under uncertainties using Dempster-Shafer theory of evidence. To demonstrate the practicability of our approach, we performed a case study using real auction data from Czech auction portal Aukro. The analysis results show that our approach can be used to detect selling stolen goods. By applying Dempster-Shafer theory to combine multiple sources of evidence for the detection of this fraudulent behavior, the proposed approach can reduce the number of false positive results in comparison to approaches using a single source of evidence.

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Evaluation of Evidence in Internet Auction Fraud Investigations

Cited by 15 publications

References 14 publications

Sensitivity Analysis of a Bayesian Network for Reasoning about Digital Forensic Evidence

Sensitivity Analysis of a Bayesian Network for Reasoning about Digital Forensic Evidence

Quantitative evaluation of the results of digital forensic investigations: a review of progress

Uncertain Reasoning for Detection of Selling Stolen Goods in Online Auctions Using Contextual Information

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