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Following the rise of e-commerce there has been a dramatic increase in online criminal activities targeting online shoppers. Considering that the number of online stores has risen dramatically, manually checking these stores has become intractable. An automated process is therefore required. We approached this problem by applying machine learning techniques to extract and detect instances of fraudulent online stores. Two sources of information were used to determine the legitimacy of an online store. First, contextual features extracted from the HTML and meta information were used to train various machine learning algorithms. Second, visual information, like the presence of social media logos, was added to make improvements on this baseline model. Results show a positive effect for adding visual information, increasing the Fl-score from 0.93 to 0.98 over the baseline model. Finally, this research shows that visual information can improve recall during web crawling.CCS CONCEPTS • Information systems → Web mining; • Computing methodologies → Machine learning.

Combining Visual and Contextual Information for Fraudulent Online Store Classification