IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i8p241-d888344.html
   My bibliography  Save this article

A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems

Author

Listed:
  • Padmalochan Panda

    (Department of Computer Science and Engineering, National Institute of Technology Jamshedpur, Jharkhand 831014, India
    These authors contributed equally to this work.)

  • Alekha Kumar Mishra

    (Department of Computer Science and Engineering, National Institute of Technology Jamshedpur, Jharkhand 831014, India
    These authors contributed equally to this work.)

  • Deepak Puthal

    (Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi 127788, United Arab Emirates
    Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Ultimo, NSW 2007, Australia
    These authors contributed equally to this work.)

Abstract

The first and foremost task of a phishing-detection mechanism is to confirm the appearance of a suspicious page that is similar to a genuine site. Once this is found, a suitable URL analysis mechanism may lead to conclusions about the genuineness of the suspicious page. To confirm appearance similarity, most of the approaches inspect the image elements of the genuine site, such as the logo, theme, font color and style. In this paper, we propose a novel logo-based phishing-detection mechanism that characterizes the existence and unique distribution of hue values in a logo image as the foundation to unambiguously represent a brand logo. Using the proposed novel feature, the detection mechanism optimally classifies a suspicious logo to the best matching brand logo. The experiment is performed over our customized dataset based on the popular phishing brands in the South-Asia region. A set of five machine-learning algorithms is used to train and test the prepared dataset. We inferred from the experimental results that the ensemble random forest algorithm achieved the high accuracy of 87% with our prepared dataset.

Suggested Citation

  • Padmalochan Panda & Alekha Kumar Mishra & Deepak Puthal, 2022. "A Novel Logo Identification Technique for Logo-Based Phishing Detection in Cyber-Physical Systems," Future Internet, MDPI, vol. 14(8), pages 1-17, August.
  • Handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:241-:d:888344
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/8/241/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/14/8/241/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rana Alabdan, 2020. "Phishing Attacks Survey: Types, Vectors, and Technical Approaches," Future Internet, MDPI, vol. 12(10), pages 1-37, September.
    2. Jimmy Moedjahedy & Arief Setyanto & Fawaz Khaled Alarfaj & Mohammed Alreshoodi, 2022. "CCrFS: Combine Correlation Features Selection for Detecting Phishing Websites Using Machine Learning," Future Internet, MDPI, vol. 14(8), pages 1-18, 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. Zhengyang Fan & Wanru Li & Kathryn Blackmond Laskey & Kuo-Chu Chang, 2024. "Investigation of Phishing Susceptibility with Explainable Artificial Intelligence," Future Internet, MDPI, vol. 16(1), pages 1-18, January.

    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. Joakim Kävrestad & Allex Hagberg & Marcus Nohlberg & Jana Rambusch & Robert Roos & Steven Furnell, 2022. "Evaluation of Contextual and Game-Based Training for Phishing Detection," Future Internet, MDPI, vol. 14(4), pages 1-16, March.
    2. Muhammad Waqas & Alishba Hania & Farzan Yahya & Iqra Malik, 2023. "Enhancing Cybersecurity: The Crucial Role of Self-Regulation, Information Processing, and Financial Knowledge in Combating Phishing Attacks," SAGE Open, , vol. 13(4), pages 21582440231, December.
    3. Ravi Kashyap, 2023. "DeFi Security: Turning The Weakest Link Into The Strongest Attraction," Papers 2312.00033, arXiv.org.
    4. Jimmy Moedjahedy & Arief Setyanto & Fawaz Khaled Alarfaj & Mohammed Alreshoodi, 2022. "CCrFS: Combine Correlation Features Selection for Detecting Phishing Websites Using Machine Learning," Future Internet, MDPI, vol. 14(8), pages 1-18, July.
    5. Kausar Yasmeen & Muhammad Adnan, 2023. "Zero-day and zero-click attacks on digital banking: a comprehensive review of double trouble," Risk Management, Palgrave Macmillan, vol. 25(4), pages 1-24, December.

    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:gam:jftint:v:14:y:2022:i:8:p:241-:d:888344. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.