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      Computer Security: 23rd European Symposium on Research in Computer Security, ESORICS 2018, Barcelona, Spain, September 3-7, 2018, Proceedings, Part I 

      Phishing Attacks Modifications and Evolutions

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          Detecting near-duplicates for web crawling

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            Tree edit distance: Robust and memory-efficient

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              Is Open Access

              Classification of Phishing Email Using Random Forest Machine Learning Technique

              Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.
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                Book Chapter
                2018
                August 08 2018
                : 243-262
                10.1007/978-3-319-99073-6_12
                7f9328ad-6136-406c-8238-d7819b9d539d
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