document: Learning to detect phishing emails Each month , more attacks are launched with the aim of making web users believe that they are communicating with a trusted entity for the purpose of stealing account information , logon credentials , and identity information in general . This attack method , commonly known as `` phishing , '' is most commonly initiated by sending out emails with links to spoofed websites that harvest information . We present a method for detecting these attacks , which in its most general form is an application of machine learning on a feature set designed to highlight user-targeted deception in electronic communication . This method is applicable , with slight modification , to detection of phishing websites , or the emails used to direct victims to these sites . We evaluate this method on a set of approximately 860 such phishing emails , and 6950 non-phishing emails , and correctly identify over 96 % of the phishing emails while only mis-classifying on the order of 0.1 % of the legitimate emails . We conclude with thoughts on the future for such techniques to specifically identify deception , specifically with respect to the evolutionary nature of the attacks and information available .
doc_bio_tags: B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O