No abstract

Internet auction fraud has become prevalent. Methodologies for detecting fraudulent transactions use historical information about Internet auction participants to decide whether or not a user is a potential fraudster. The information includes reputation scores, values of items, time frames of various activities and transaction records. This paper presents a distinctive set of fraudster characteristics based on an analysis of 278 allegations about the sale of counterfeit goods at Internet auction sites. Also, it applies a Bayesian approach to analyze the relevance of evidence in Internet auction fraud cases.

No abstract

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.

An operational complexity model (OCM) is proposed to enable the complexity of both the cognitive and the computational components of a process to be determined. From the complexity of formation of a set of traces via a specified route a measure of the probability of that route can be determined. By determining the complexities of alternative routes leading to the formation of the same set of traces, the odds indicating the relative plausibility of the alternative routes can be found. An illustrative application to a BitTorrent piracy case is presented, and the results obtained suggest that the OCM is capable of providing a realistic estimate of the odds for two competing hypotheses. It is also demonstrated that the OCM can be straightforwardly refined to encompass a variety of circumstances.