E-fencing detection: mining online classified ad websites for stolen property.
Date
2014-09-01
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Abstract
With the emergence of e-fencing, there presents a need to automate both the
detection of ads selling stolen property and the reporting process for victims. This thesis
presents a framework that dynamically retrieves and classifies online ads utilizing
artificial intelligence (AI) to minimize amount of domain knowledge required. Evaluating
these ads against existing known characteristics of theft as well as extracting new
characteristics from suspicious ads. This in conjunction with a reporting system allows
users to report events of theft and matches them to previously classified ads. The
framework was designed such that it would be domain portable and allow for rapid
adaptation to other domains. Experiments showed promising results, correctly classifying
single and multiple trend datasets, displaying anomalies in price histograms, and
extracting potential patterns that explain price variance. Experiments on other domains
highly susceptible to scams displayed unique results contradicting some fundamental
assumptions of the behavior of thieves.
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Keywords
E-fencing, Stolen property, Classification problem, Genetic algorithms, Rule extraction