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Scope of Visual-Based Similarity Approach Using Convolutional Neural Network on Phishing Website Detection

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Intelligent System Design

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1171))

Abstract

Phishing website is an illegitimate website that is designed by dishonest people to mimic a real website. Those who are entering such a website may expose their sensitive information to the attacker who might use this information for financial and criminal activities. In this technological world, phishing websites are created using new techniques allows them to escape from most anti-phishing tool. So, the white list and blacklist based techniques are less effective when compared with the recent phishing trends. Advanced to that, there exist some tools using machine learning and deep learning approaches by examining webpage content in order to detect phishing websites. Along with the rapid growth of phishing technologies, it is needed to improve the effectiveness and efficiency of phishing website detection. This work reviewed many papers that proposed different real-time as well as non-real-time techniques. As the result, this study suggests a Convolutional Neural Network (CNN) framework with 18 layers and scope of transfer learning in Alex Net for the classification of websites using screenshot images and URLs of phishing and legitimate websites. CNN is a class of deep, feed-forward artificial neural networks (where connections between nodes do not form a cycle) & use a variation of multilayer perceptions designed to require minimal preprocessing.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Teegala Krishna Reddy Engineering College, Hyderabad, India

    J. Rajaram & M. Dhasaratham

Authors
  1. J. Rajaram
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  2. M. Dhasaratham
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Corresponding author

Correspondence to J. Rajaram .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Demmed to be University, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    B. Janakiramaiah

  4. College of Information Science and Engineering, Ritsumeikan University, Kyoto, Japan

    Yen-Wei Chen

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Rajaram, J., Dhasaratham, M. (2021). Scope of Visual-Based Similarity Approach Using Convolutional Neural Network on Phishing Website Detection. In: Satapathy, S., Bhateja, V., Janakiramaiah, B., Chen, YW. (eds) Intelligent System Design. Advances in Intelligent Systems and Computing, vol 1171. Springer, Singapore. https://doi.org/10.1007/978-981-15-5400-1_45

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