IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v68y2018i4d10.1007_s11235-017-0414-0.html
   My bibliography  Save this article

Towards detection of phishing websites on client-side using machine learning based approach

Author

Listed:
  • Ankit Kumar Jain

    (National Institute of Technology Kurukshetra)

  • B. B. Gupta

    (National Institute of Technology Kurukshetra)

Abstract

The existing anti-phishing approaches use the blacklist methods or features based machine learning techniques. Blacklist methods fail to detect new phishing attacks and produce high false positive rate. Moreover, existing machine learning based methods extract features from the third party, search engine, etc. Therefore, they are complicated, slow in nature, and not fit for the real-time environment. To solve this problem, this paper presents a machine learning based novel anti-phishing approach that extracts the features from client side only. We have examined the various attributes of the phishing and legitimate websites in depth and identified nineteen outstanding features to distinguish phishing websites from legitimate ones. These nineteen features are extracted from the URL and source code of the website and do not depend on any third party, which makes the proposed approach fast, reliable, and intelligent. Compared to other methods, the proposed approach has relatively high accuracy in detection of phishing websites as it achieved 99.39% true positive rate and 99.09% of overall detection accuracy.

Suggested Citation

  • Ankit Kumar Jain & B. B. Gupta, 2018. "Towards detection of phishing websites on client-side using machine learning based approach," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(4), pages 687-700, August.
    • Handle: RePEc:spr:telsys:v:68:y:2018:i:4:d:10.1007_s11235-017-0414-0
    DOI: 10.1007/s11235-017-0414-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-017-0414-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11235-017-0414-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Naoual El Aboudi & Laila Benhlima, 2017. "Parallel and Distributed Population based Feature Selection Framework for Health Monitoring," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 7(1), pages 57-71, January.
    2. Shashank Gupta & B. B. Gupta, 2017. "Detection, Avoidance, and Attack Pattern Mechanisms in Modern Web Application Vulnerabilities: Present and Future Challenges," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 7(3), pages 1-43, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ömer Kasim, 2021. "Automatic detection of phishing pages with event-based request processing, deep-hybrid feature extraction and light gradient boosted machine model," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 78(1), pages 103-115, September.
    2. Abdul Basit & Maham Zafar & Xuan Liu & Abdul Rehman Javed & Zunera Jalil & Kashif Kifayat, 2021. "A comprehensive survey of AI-enabled phishing attacks detection techniques," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(1), pages 139-154, January.
    3. Yilin Wang & Siqing Xue & Jun Song, 2022. "A Malicious Webpage Detection Method Based on Graph Convolutional Network," Mathematics, MDPI, vol. 10(19), pages 1-15, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jolyane, Blouin-Bougie & Nabil, Amara, 2023. "Breast cancer risk prediction models’ adoption by Canadian providers - an in-depth qualitative comparative analysis," Journal of Business Research, Elsevier, vol. 157(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:telsys:v:68:y:2018:i:4:d:10.1007_s11235-017-0414-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.