• Pulkit Garg
• Kartikay
• Ankush Kalsotra
• B. Mohit Manihara
• Padmashree T

Phishing websites represent a substantial cybersecurity threat, resulting in financial losses, data breaches, and compromised user privacy. Traditional detection methods are struggling to keep up with the constantly evolving tactics of cybercriminals. Thus, employing machine learning emerges as the most effective approach for automatically identifying phishing websites. In this the dataset used is from Kaggle, consists of 31 features and 11054 rows describing them with both phishing and legitimate data, 80:20 split for training and testing. Six machine learning classifiers are incorporated to train the data that are logistic regression, gradient boost, naive bayes classifier, decision tree, random forest and support vector machine with the highest accuracy of 97.4 using the gradient boost classifier.

Phishing, machine learning, legitimate, cybersecurity, classifier.

"Phishing Website Detection using Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.d502-d506, June-2024, Available :http://www.jetir.org/papers/JETIR2406361.pdf

"Phishing Website Detection using Machine Learning Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppd502-d506, June-2024, Available at : http://www.jetir.org/papers/JETIR2406361.pdf