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Phishing Detection Model for Emails Using Classification Algorithm

Authors: ADEKUNLE Yinka , Olu-oshadare Olumide

Publication Type: Journal article

Journal: Research Journal Of Mathematics And Computer Science

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Abstract

Anti-Phishing Working Group (APWG) is a contributing member that report, and study the ever-evolving nature and techniques of cybercrime. The APWG tracks the number of unique phishing emails and web sites, a primary measure of phishing across the globe. A single phishing site may be advertised as thousands of customized features, all leading to basically the same attack destination. This work aims to design a machine learning model using a hybrid of two classification algorithms which include Random Forests and Support Vector Machine (SVM). Also perform feature selection on the obtained phishing dataset to select a subset of highly predictive features and evaluate the model against other classification algorithms and existing solutions with the following metrics: False Positive Rate (FPR), Accuracy, Area Under the Receiver Operating Characteristic Curve (AUCROC) and Weighted Averages. It is expected that upon evaluation of this model much improved efficiency would be recorded as against other existing models.

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ADEKUNLE,Y. Olu-oshadare,O. . (2020). Phishing Detection Model for Emails Using Classification Algorithm, 4 (), 1-1.

ADEKUNLE,Y. Olu-oshadare,O. . "Phishing Detection Model for Emails Using Classification Algorithm" 4, no (), (2020): 1-1.

ADEKUNLE,Y. and Olu-oshadare,O. and . (2020). Phishing Detection Model for Emails Using Classification Algorithm, 4 (), pp1-1.

ADEKUNLEY, Olu-oshadareO, . Phishing Detection Model for Emails Using Classification Algorithm. 2020, 4 ():1-1.

ADEKUNLE,Yinka , and Olu-oshadare,Olumide . "Phishing Detection Model for Emails Using Classification Algorithm", 4 . (2020) : 1-1.

A.Yinka & O.Olumide , "Phishing Detection Model for Emails Using Classification Algorithm" vol.4, no., pp. 1-1, 2020.

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