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DS Journal of Cyber Security (DS-CYS)

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Volume 2 | Issue 3 | Year 2024 | Article Id: CYS-V2I3P102 DOI: https://doi.org/10.59232/CYS-V2I3P102

Phishing Attack Detection Using Machine Learning

Olaniyi. A. Ayeni, Hope O. Akinyemi

ReceivedRevisedAcceptedPublished
15 Jul 202420 Aug 202418 Sep 202430 Sep 2024

Citation

Olaniyi. A. Ayeni, Hope O. Akinyemi. “Phishing Attack Detection Using Machine Learning.” DS Journal of Cyber Security, vol. 2, no. 3, pp. 15-25, 2024.

Abstract

Phishing acquires sensitive information, like login credentials, i.e. usernames, passwords or other security tokens, card information, etc, from users. In most cases, Phishing does not require high technicality, making it the third most performed attack according to multiple sources. Despite many solutions being proposed to completely eradicate or at least mitigate about 80% of Phishing attacks, Phishing continues to be prevalent. Vishakha P.R. & Sahil S.J. (2020) were unable to detect web pages automatically, and the systems were limited to system applications alone. Therefore, this work aims to design a system for detecting phishing attacks, Implement the design, and evaluate the algorithm’s performance. Jupyter Notebook was the medium used for analysis. The two algorithms used were two-class logistic detection and Naïve Bayes. The dataset (phishing.csv) obtained from Kaggle was divided into two datasets to train and test in the ratio 80:20. The result of two class logistic regression was 94.2% compared to 85% test accuracy by the Naïve Bayes algorithm.

Keywords

Phishing detection, Naïve Bayes, Machine Learning, Logistic regression, Cyber production.

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Phishing Attack Detection Using Machine Learning