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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
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    References listed on IDEAS

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    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.
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    Citations

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    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. 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.
    3. 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.

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