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Publication Type : Conference Paper
Publisher : CEUR Workshop Proceedings
Source : CEUR Workshop Proceedings, CEUR-WS, Volume 2124, p.64-68 (2018)
Keywords : Artificial intelligence, Computer crime, Domain knowledge, Electronic mail, Email Detection, Feature engineerings, Formulated problems, Learning algorithms, Learning systems, Lexical features, Machine learning techniques, Self learning system, Term-document matrixes
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2018
Abstract : Phishing email detection is a significant threat in today's world. The rate at which phishing are generated are tremendously increasing day by day. It is high time to deploy a self-learning system that gives a time bound detection and prevention of phishing email efficiently. This work proposes a system which uses term document matrix as feature engineering mechanism and classical machine learning techniques for detecting phishing email from legitimate and phishing ones. The system also incorporates the domain knowledge and lexical features as part of feature engineering mechanism. The efficiency of the system is compared using different classical machine learning techniques. Based on the accuracy, we propose the best model that solves the formulated problem efficiently. Copyright © by the paper's authors.
Cite this Research Publication : N. A. Unnithan, Harikrishnan, N. B., Akarsh, S., Vinayakumar, R., and Dr. Soman K. P., “Machine learning based phishing E-mail detection Security-CEN@Amrita”, in CEUR Workshop Proceedings, 2018, vol. 2124, pp. 64-68.