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Erschienen in:
Towards a New Evolutionary Subsampling Technique for Heuristic Optimisation of Load Disaggregators Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Phishing Detection on Twitter Streams

verfasst von : Se Yeong Jeong, Yun Sing Koh, Gillian Dobbie

Erschienen in: Trends and Applications in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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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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Metadaten
Titel
Phishing Detection on Twitter Streams
verfasst von
Se Yeong Jeong
Yun Sing Koh
Gillian Dobbie
Copyright-Jahr
2016
Buch
Trends and Applications in Knowledge Discovery and Data Mining

Print ISBN: 978-3-319-42995-3

Electronic ISBN: 978-3-319-42996-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-42996-0_12