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Learning-Based Model for Phishing Attack Detection

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Artificial Intelligence in Cyber Security: Theories and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 240))

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Abstract

The growing amount of phishing outbreaks is one of the main concern for security scientists nowadays. Signature-based approaches are used in the traditional methods for categorizing phishing websites. Such tools and techniques fail with advanced and complex phishing webpage. Consequently, learning-based algorithms are widely adopted in many industries. Such algorithms can be applied to analyze phishing websites. The detection accuracy can be increased with a large dataset and complex features. This chapter addresses the problem of analyzing such phishing activities on web pages. Explicitly, we propose a machine learning-based framework with various supervised learning algorithms such as Random Forest, Support Vector Machine, and Decision Trees. We perform the hyperparameter optimization of these algorithms using sci-kit-learn machine learning frameworks. In the end, we discussed the applied implementation of classifying the phishing attack on the real-world dataset available on Kaggle.

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Author information

Authors and Affiliations

  1. Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USA

    Jayesh Soni & Nagarajan Prabakar

  2. Applied Research Center, Florida International University, Miami, FL, USA

    Himanshu Upadhyay

  3. Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA

    Surya Sirigineedi

  4. Thornton Tomasetti, Miami, FL, USA

    Krishna Sai Vutukuru

  5. University Of Central Missouri, Warrensburg, USA

    S. S. ChandanaEswari Sirigineedi

Authors
  1. Jayesh Soni
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  2. Surya Sirigineedi
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  3. Krishna Sai Vutukuru
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  4. S. S. ChandanaEswari Sirigineedi
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  5. Nagarajan Prabakar
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  6. Himanshu Upadhyay
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Corresponding author

Correspondence to Jayesh Soni .

Editor information

Editors and Affiliations

  1. Applied Research Center, Florida International University, Miami Fl, FL, USA

    Tushar Bhardwaj

  2. Applied Research Center, Florida International University, Miami Fl, FL, USA

    Himanshu Upadhyay

  3. Shobhit University, Gangoh, India

    Tarun Kumar Sharma

  4. Department of Computer Science, Creighton University, Omaha, NE, USA

    Steven Lawrence Fernandes

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Soni, J., Sirigineedi, S., Vutukuru, K.S., Sirigineedi, S.S.C., Prabakar, N., Upadhyay, H. (2023). Learning-Based Model for Phishing Attack Detection. In: Bhardwaj, T., Upadhyay, H., Sharma, T.K., Fernandes, S.L. (eds) Artificial Intelligence in Cyber Security: Theories and Applications. Intelligent Systems Reference Library, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-031-28581-3_11

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