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Using Genetic Algorithms to Optimized Stacking Ensemble Model for Phishing Websites Detection

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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 affect individuals and organisations websites. I addition, it may also devices and networks such as Internet of Things (IoT) devices and networks. IoT networks are exposed environment for phishing attacks. Thingbots software in IoT devices can be utilized by attackers for spreading hidden and unnoticed spam emails. Several approaches such as machine learning, deep learning and others were used to create and design detection methods for phishing attacks. However, these methods detection accuracy still not enough and need to be enhanced. Anew proposed method for phishing website detection that based on optimized ensemble classification method is suggested in the present study. in this proposed method, A Genetic Algorithm (GA) was used to optimize the ensemble classification method by tuning the parameters of several ensemble machine learning methods, including Bagging, GradientBoost, and LightGBM. These were accomplished by ranking the optimized classifiers to pick out the best three models as base classifiers of a stacking ensemble method. A dataset of websites that made up of 44% phishing websites (4898) and 5\% legitimate websites (6157) was used for the present study experiments. As a come out, with the proposed detection method, 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

    Zeyad Ghaleb Al-Mekhlafi & Badiea Abdulkarem Mohammed

  2. College of Computer Sciences and Engineering, Hodeidah University, 967, Hodeidah, Yemen

    Badiea Abdulkarem Mohammed

Authors
  1. Zeyad Ghaleb Al-Mekhlafi
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  2. Badiea Abdulkarem Mohammed
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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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Al-Mekhlafi, Z.G., Mohammed, B.A. (2021). Using Genetic Algorithms to Optimized Stacking Ensemble Model for Phishing Websites Detection. 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_27

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  • DOI: https://doi.org/10.1007/978-981-16-8059-5_27

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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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