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Optimized Stacking Ensemble Model to Detect Phishing Websites

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Advances in Cyber Security (ACeS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1487))

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Abstract

Phishing attacks are security attacks that do not affect only individuals or organisations websites, but it may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for dispersal hidden junk emails that not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there still a need to enhance the detection accuracy. An optimized ensemble classification method for phishing website detection is proposed in this study. A Genetic Algorithm (GA) was used to optimize the ensemble classification method by tuning the parameters of several ensemble Machine Learning (ML) methods, including Random Forest, AdaBoost, and XGBoost. These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for stacking ensemble method. A phishing website dataset that made up of 4898 phishing websites and 6157 legitimate websites was used for this study experiments. As a result, detection accuracy was enhanced and reached 97.16%.

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Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, University of Ha’il, Ha’il, 81481, Saudi Arabia

    Badiea Abdulkarem Mohammed & Zeyad Ghaleb Al-Mekhlafi

  2. College of Computer Sciences and Engineering, Hodeidah University, Hodeidah, Yemen

    Badiea Abdulkarem Mohammed

Authors
  1. Badiea Abdulkarem Mohammed
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  2. Zeyad Ghaleb Al-Mekhlafi
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Corresponding author

Correspondence to Badiea Abdulkarem Mohammed .

Editor information

Editors and Affiliations

  1. Hodeidah University, Hodeidah, Yemen

    Nibras Abdullah

  2. Universiti Sains Malaysia, Penang, Malaysia

    Selvakumar Manickam

  3. Universiti Sains Malaysia, Penang, Malaysia

    Mohammed Anbar

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Mohammed, B.A., Al-Mekhlafi, Z.G. (2021). Optimized Stacking Ensemble Model to Detect Phishing Websites. In: Abdullah, N., Manickam, S., Anbar, M. (eds) Advances in Cyber Security. ACeS 2021. Communications in Computer and Information Science, vol 1487. Springer, Singapore. https://doi.org/10.1007/978-981-16-8059-5_23

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  • DOI: https://doi.org/10.1007/978-981-16-8059-5_23

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8058-8

  • Online ISBN: 978-981-16-8059-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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