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Publication Type : Conference Paper
Publisher : CEUR Workshop Proceedings
Source : CEUR Workshop Proceedings, CEUR-WS, Volume 2124, p.21-28 (2018)
Keywords : Adaptive boosting, Artificial intelligence, Comparative studies, Computer crime, Decision trees, Electronic mail, Email Detection, Learning systems, Machine learning approaches, Machine learning techniques, Naive Bayes, Phishing, Random forests
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2018
Abstract : Email is a platform where we communicate, exchange ideas between each other. In today's world email plays a key role irrespective of the field. In such a scenario, phishing mails are one of the major threats in today's world. These e-mails”seems” like legitimate but leads the users to malicious sites. As a result the user or organization or institution end up as the prey of the online predators. In order to tackle such problems, several statistical methods have been applied. In this paper we make use of distributional representation namely TF-IDF for numeric representation of phishing mails. Also a comparative study of classical machine learning techniques like Random Forest, AdaBoost, Naive Bayes, Decision Tree, SVM. Copyright © by the paper's authors.
Cite this Research Publication : N. B. Harikrishnan, Vinayakumar, R., and Dr. Soman K. P., “A machine learning approach towards phishing email detection CEN-Security@IWSPA 2018”, in CEUR Workshop Proceedings, 2018, vol. 2124, pp. 21-28.