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Publication Type : Conference Paper
Publisher : CEUR Workshop Proceedings
Source : CEUR Workshop Proceedings, CEUR-WS, Volume 2124, p.69-76 (2018)
Keywords : Computer crime, Electronic mail, Factorization, Financial activity, information dissemination, Information retrieval systems, Machine learning techniques, Nonnegative matrix factorization, Number of peoples, Phishing emails, Sensitive informations, Singular value decomposition, Supervised classification, Supervised learning, Term-document matrixes
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2018
Abstract : In the modern era, all services are maintained online and everyone use it to speed up their day to day activities. This include social as well as financial activities which involves usage of sensitive information to carry out the intended task. With the increase in usage of such facilities put forth the importance of securing the data used to perform such actions. Over the last decade phishing has become a serious threat to the society by stealing sensitive information to get hold of these facilities. This is considered to be the most profitable cybercrime and according to IBMs X-Force researchers statistics, the number of people becoming the victim of such activities are increasing tremendously. As the risk of phishing emails are increasing steadily, the need to detect and overcome such situations stands as one of the highest priority task at hand. In the present work, we will use non-sequential representation such as term document matrix approach followed by Singular Value Decomposition (SVD) and Nonnegative Matrix Factorization (NMF) to model phishing email detection as a supervised classification problem to detect phishing emails from legitimate ones. Copyright © by the paper's authors.
Cite this Research Publication : A. Vazhayil, Harikrishnan, N. B., Vinayakumar, R., and Dr. Soman K. P., “PED-ML: Phishing email detection using classical machine learning techniques CENSec@Amrita”, in CEUR Workshop Proceedings, 2018, vol. 2124, pp. 69-76.