AAAI Publications, The Twenty-Sixth International FLAIRS Conference

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Discovering Fraud in Online Classified Ads
Alan Matthew McCormick, William Eberle

Last modified: 2013-05-19

Abstract


Classified ad sites routinely process hundreds of thousands to millions of posted ads, and only a small percentage of those may be fraudulent. Online scammers often go through a great amount of effort to make their listings look legitimate. Examples include copying existing advertisements from other services, tunneling through local proxies, and even paying for extra services using stolen account information. This paper focuses on applying knowledge discovery concepts towards the detection of online, classified fraud. Traditional data mining is used to extract relevant attributes from an online classified advertisements database and machine learning algorithms are applied to discover patterns and relationships of fraudulent activity. With our proposed approach, we will demonstrate the effectiveness of applying data mining techniques towards the detection of fraud in online classified advertisements.

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