@article {10.3844/jcssp.2025.1168.1175,
article_type = {journal},
title = {Apriori-Based Analysis of Website Phishing},
author = {Gortifacion, Rene Clint and Malangsa, Rhoderick and Diola, Adelfa and Junior, Tamar Mejia},
volume = {21},
number = {5},
year = {2025},
month = {May},
pages = {1168-1175},
doi = {10.3844/jcssp.2025.1168.1175},
url = {https://thescipub.com/abstract/jcssp.2025.1168.1175},
abstract = {Phishing attacks put users' sensitive information at serious risk and are a rising concern in cybersecurity. In order to minimize the possible damage brought on by these assaults, it is essential to detect and categorize phishing websites correctly. In this study, we provide an Apriori-based analysis method for identifying and categorizing website phishing. The Apriori algorithm, which is frequently used in association rule mining, provides a distinctive viewpoint for examining the traits and patterns of phishing websites. This study aims to find significant associations that can help distinguish between legal and phishing websites by using the Apriori algorithm to a dataset of website attributes and related phishing labels. An extensive collection of website labels and attributes, including URL structure, HTML content analysis and other behavioral indicators from UCI, was gathered for the study. We compared the effectiveness of the Apriori-based approach to other phishing detection techniques now in use, such as other machine learning algorithms. In order to create the best rules for this study, the researchers chose to alter the 11,000 datasets run on Weka Software using the Apriori Algorithm. Further, the researchers developed the ten best rules for association on how the Apriori algorithm may be utilized to improve phishing attack detection. This study could improve web security protocols and help prevent phishing attempts, protecting user data and lessening the financial toll of cybercrime.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}