A hybrid firefly and support vector machine classifier for phishing email detection

Purpose – Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars has been lost by many companies and individuals. The global impact of phishing attacks will continue to be on the increase and thus a more efficient phishing dete...

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Bibliographic Details
Published in:Kybernetes 2016-06, Vol.45 (6), p.977-994
Main Authors: Adewumi, Oluyinka Aderemi, Akinyelu, Ayobami Andronicus
Format: Article
Language:English
Subjects:
Algorithms
Classification
Classifiers
Computer information security
Computer simulation
Cybercrime
Datasets
Electronic mail
Email
Feature extraction
Fraud
Heuristic methods
Information & knowledge management
Information systems
Machine learning
Mail
Phishing
Support vector machines
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Summary:Purpose – Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars has been lost by many companies and individuals. The global impact of phishing attacks will continue to be on the increase and thus a more efficient phishing detection technique is required. The purpose of this paper is to investigate and report the use of a nature inspired based-machine learning (ML) approach in classification of phishing e-mails. Design/methodology/approach – ML-based techniques have been shown to be efficient in detecting phishing attacks. In this paper, firefly algorithm (FFA) was integrated with support vector machine (SVM) with the primary aim of developing an improved phishing e-mail classifier (known as FFA_SVM), capable of accurately detecting new phishing patterns as they occur. From a data set consisting of 4,000 phishing and ham e-mails, a set of features, suitable for phishing e-mail detection, was extracted and used to construct the hybrid classifier. Findings – The FFA_SVM was applied to a data set consisting of up to 4,000 phishing and ham e-mails. Simulation experiments were performed to evaluate and compared the performance of the classifier. The tests yielded a classification accuracy of 99.94 percent, false positive rate of 0.06 percent and false negative rate of 0.04 percent. Originality/value – The hybrid algorithm has not been earlier apply, as in this work, to the classification and detection of phishing e-mail, to the best of the authors’ knowledge.
ISSN:0368-492X
1758-7883
DOI:10.1108/K-07-2014-0129