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Analysis of Phishing Base Problems Using Random Forest Features Selection Techniques and Machine Learning Classifiers

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Data Intelligence and Cognitive Informatics

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Since the Internet is anonymous and uncontrolled, it is more open to phishing attacks, which can trick users to view malicious content in exchange for their personal information. However, the number of victims to this digital attack is significantly increasing due to inadequate security mechanisms. This research study develops a cyberbullying detection system, which can produce features from Twitter text by incorporating a point-wise mutual information approach. Further, a supervised machine learning method is developed for detecting the cyberbullying scenarios. Moreover, the proposed study has employed the sentiment, lexicon, and embedding features along with the PMI-semantic orientation. To apply extracted features, the SVM, Naive Bayes, KNN, decision tree, and random forest algorithm were employed. Experiments employing the proposed framework in a multi-class and binary setting indicate considerable potential in terms of kappa values, increased accuracy, and computed f-values. These findings imply that the proposed framework is a suitable option for recognizing the cyberbullying behavior in online social networks. Finally, the proposed outcomes and baseline features are compared by using various machine learning algorithms. The tenfold cross-validation has generated a highest accuracy of about 90.36%, and all four experiments assessed random forest algorithm based on 80% of the training dataset. The test result has also computed higher accuracy on random forest algorithm based on 20% of the test dataset.

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Authors and Affiliations

  1. Department of Computer Applications, VBS Purvanchal University, Jaunpur, India

    Mithilesh Kumar Pandey, Munindra Kumar Singh & Saurabh Pal

  2. Department of Electronics and Communication, VBS Purvanchal University, Jaunpur, India

    B. B. Tiwari

Authors
  1. Mithilesh Kumar Pandey
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  2. Munindra Kumar Singh
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  3. Saurabh Pal
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  4. B. B. Tiwari
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Corresponding author

Correspondence to Saurabh Pal .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, GITAM University, Bangalore, Karnataka, India

    I. Jeena Jacob

  2. Department of Mathematics and Computer Science, Ashland University, River Forest, IL, USA

    Selvanayaki Kolandapalayam Shanmugam

  3. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Ivan Izonin

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Pandey, M.K., Singh, M.K., Pal, S., Tiwari, B.B. (2023). Analysis of Phishing Base Problems Using Random Forest Features Selection Techniques and Machine Learning Classifiers. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-6004-8_5

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