Abstract
Phishing has remained a serious threat to cybersecurity, as this type of attack can easily bypass detection systems that are either rule-based or blacklist-based. The proposed thesis work will present a solution that will make use of both statistical analysis and machine learning to correctly identify a phishing website. The solution will make use of a hybrid approach comprising statistical-based preprocessing methodologies, such as PCA and decision tree-based feature selection, to filter the crucial URL features that a website may possess. A carefully balanced dataset has been utilized, as well as a non-parametric approach utilizing the Mann-Whitney U-test to validate if the features are statistically important. The thesis will conclude by building a neural network model capable of classifying a phishing website with accuracy through optimized URL features.
Publication Date
12-2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research
Advisor
Sanjay Modak
Advisor/Committee Member
Hammou Messatfa
Recommended Citation
Alfalasi, Suhail Othman, "PREDICTIVE AI MODELS FOR PHISHING ATTACK DETECTION: A DATA-DRIVEN AND STATISTICAL ANALYSIS APPROACH" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12393
Campus
RIT Dubai
