Abstract
This paper presents a novel method for mining suspicious websites from World Wide Web by using state-of-the-art pattern mining and machine learning methods. In this document, the term “suspicious website” is used to mean any website that contains known or suspected violations of law. Although, we present our evaluation on illegal online organ trading, the method described in this paper is generic and can be used to detect any specific kind of websites. We use an iterative setting in which at each iterations we unearth both normal and suspicious websites. These newly detected websites are augmented in our training examples and used in next iterations. The first iteration uses user supplied seed normal and suspicious websites. We show that the accuracy increases in intial iterations but decreases with further increase in iterations. This is due to the bias caused by adding large number of normal websites and also due to the automatic addition of noise in training examples.
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Jung Pandey, S., Manandhar, S., Kleszcz, A. (2013). Using Sub-sequence Patterns for Detecting Organ Trafficking Websites. In: Dziech, A., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2013. Communications in Computer and Information Science, vol 368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38559-9_15
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DOI: https://doi.org/10.1007/978-3-642-38559-9_15
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