Abstract
With the prevalence of cutting-edge technology, the social media network is gaining popularity and is becoming a worldwide phenomenon. Twitter is one of the most widely used social media sites, with over 500 million users all around the world. Along with its rapidly growing number of users, it has also attracted unwanted users such as scammers, spammers and phishers. Research has already been conducted to prevent such issues using network or contextual features with supervised learning. However, these methods are not robust to changes, such as temporal changes or changes in phishing trends. Current techniques also use additional network information. However, these techniques cannot be used before spammers form a particular number of user relationships. We propose an unsupervised technique that detects phishing in Twitter using a 2-phase unsupervised learning algorithm called PDT (Phishing Detector for Twitter). From the experiments we show that our technique has high accuracy ranging between 0.88 and 0.99.
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Jeong, S.Y., Koh, Y.S., Dobbie, G. (2016). Phishing Detection on Twitter Streams. In: Cao, H., Li, J., Wang, R. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9794. Springer, Cham. https://doi.org/10.1007/978-3-319-42996-0_12
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DOI: https://doi.org/10.1007/978-3-319-42996-0_12
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