The objective of this study is to improve the detection of phishing attacks, which aim to steal sensitive information from unsuspecting users, by comparing two machine learning algorithms: k-Nearest Neighbors (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k - N N</tex>) and RF. In the study, we used a dataset of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1 2 5</tex> images to evaluate both algorithms. Out of these images, 70% (or 130 frames) were allocated for training the models, while the remaining 30% were used for testing their performance. Each algorithm was tested through 20 separate iterations to ensure reliability. The accuracy of each algorithm was assessed using statistical analysis, with a G-Power value set at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8 0 %</tex> for significance. The RF algorithm showed superior performance, achieving a high detection accuracy rate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9 0. 5 4 %</tex> for phishing attacks. This result was statistically significant with a p-value of 0.04. In comparison, the k-NN algorithm did not perform as well. Overall, the study finds that the RF algorithm is more effective than k-NN in detecting phishing attacks, making it a better choice for enhancing cybersecurity measures.
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Ganesh et al. (2024) studied this question.