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Evolution in Computational Intelligence pp 55–64Cite as

Machine Learning Techniques for the Investigation of Phishing Websites

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1176))

Abstract

Phishing is ordinarily acquainted with increase a position in an organization or administrative systems as a zone of a greater assault, similar to an advanced tireless risk (APT) occasion. An association surrendering to such a partner degree assault generally continues serious money related misfortunes furthermore to declining piece of the pie, notoriety, and customer trust. Depending on scope, a phishing attempt may step up into a security episode from that a business can have an inconvenient time recuperating. So as to locate this kind of assault, we endeavored to make a machine learning model that advises the client that it is suspicious or genuine. Phishing sites contain various indications among their substance also, web program-based information. The motivation behind this investigation is to perform different AI-based order for 30 features incorporating Phishing Websites Data in the UC Irvine AI Repository database. For results appraisal, random forest (RF) was contrasted and elective machine learning ways like linear regression (LR), support vector machine (SVM), Naive Bayes (NB), gradient boosting classifier (GBM), artificial neural network (ANN) and recognized to have the most noteworthy exactness of 97.39.

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Author information

Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Karnataka Surathkal, Mangaluru, Karnataka, 575025, India

    Ajaykumar K. B. & Bhawana Rudra

Authors
  1. Ajaykumar K. B.
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  2. Bhawana Rudra
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Corresponding author

Correspondence to Ajaykumar K. B. .

Editor information

Editors and Affiliations

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

    Vikrant Bhateja

  2. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Sheng-Lung Peng

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  4. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Ajaykumar K. B., Rudra, B. (2021). Machine Learning Techniques for the Investigation of Phishing Websites. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_6

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