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ISSN 2063-5346
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HIGHER ACCURACY OF DETECTING PHISHING WEBSITES USING DECISION TREE ALGORITHM COMPARING WITH LOGISTIC REGRESSION ALGORITHM

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Panga Satheesh, K. Malathi
» doi: 10.31838/ecb/2023.12.sa1.305

Abstract

Aim: The main objective of the research study is to improve the accuracy for Detecting phishing websites using the Decision Tree Algorithm against Logistic Regression machine learning algorithm. Materials and Methods: The study used 20 samples with two groups of algorithms with the G-power value of 80% percent and the phishing attack data were collected from various web sources with recent study findings and threshold 0.05 and confidence interval 96.49% with mean and standard deviation. To predict the phishing attacks by improving the Logistic Regression Algorithm has found 92.65% of accuracy, therefore this study needs to find the better accuracy for Phishing Attack prediction with the Decision Tree Algorithm machine learning algorithm. Result: This research study found 96.59% of accuracy for Detecting phishing websites using the Decision Tree algorithm with a significant value of two tailed tests is 0.002 (p<0.05) with 96.49% confidence interval. Conclusion: This study concludes that the Decision Tree algorithm on Innovative phishing website Detection is significantly better than the Logistic Regression algorithm.

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