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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
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    References listed on IDEAS

    as
    1. 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.
    2. Rana Alabdan, 2020. "Phishing Attacks Survey: Types, Vectors, and Technical Approaches," Future Internet, MDPI, vol. 12(10), pages 1-37, September.
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    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.

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