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Phishing website detection using support vector machines and nature-inspired optimization algorithms

Author

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  • Sagnik Anupam

    (DPS RK Puram)

  • Arpan Kumar Kar

    (Indian Institute of Technology)

Abstract

Phishing websites are amongst the biggest threats Internet users face today, and existing methods like blacklisting, using SSL certificates, etc. often fail to keep up with the increasing number of threats. This paper aims to utilise different properties of a website URL, and use a machine learning model to classify websites as phishing and non-phishing. These properties include the IP address length, the authenticity of the HTTPs request being sent by the website, usage of pop-up windows to enter data, Server Form Handler status, etc. A Support Vector Machine binary classifier trained on an existing dataset has been used to predict if a website was a legitimate website or not, by finding an optimum hyperplane to separate the two categories. This optimum hyperplane is found with the help of four optimization algorithms, the Bat Algorithm, the Firefly Algorithm, the Grey Wolf Optimiser algorithm and the Whale Optimization Algorithm, which are inspired by various natural phenomena. Amongst the four nature-inspired optimization algorithms, it has been determined that the Grey Wolf Optimiser algorithm’s performance is significantly better than that of the Firefly Algorithm, but there is no significant difference while comparing the performance of any other pair of algorithms. However, all four nature-inspired optimization algorithms perform significantly better than the grid-search optimized Random Forest classifier model described in earlier research.

Suggested Citation

  • Sagnik Anupam & Arpan Kumar Kar, 2021. "Phishing website detection using support vector machines and nature-inspired optimization algorithms," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(1), pages 17-32, January.
    • Handle: RePEc:spr:telsys:v:76:y:2021:i:1:d:10.1007_s11235-020-00739-w
    DOI: 10.1007/s11235-020-00739-w
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    References listed on IDEAS

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    1. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
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    Cited by:

    1. Zhang Ling & Zhang Jia Hao, 2022. "Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 18(1), pages 1-24, January.

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