Abstract: Phishing websites that anticipate to take the victim’s confidential data by diverting them to surf a fake website page that resembles a sincere to goodness one is some another type of criminal activity through the internet and its one of the especially concerns in numerous areas including e-managing an account and retailing. Detecting phishing sites is a complex and unpredictable process involving numerous variables and criteria that are not stable. Using Extreme Learning Machines, we proposed an intelligent model for detecting phishing web pages. There are different types of web pages with different features. Therefore, we must use a specific set of features on web pages to protect against phishing. A machine learning model was proposed to detect phishing web pages. To detect phishing web pages, we proposed a machine learning model. This study aims to detect phishing URLs and narrow down the best machine learning method based on accuracy, false-positive rate, and false-negative rate. Phishing, Feature Classification, Random Forest Classifier, and other terms are used in this study.
Keywords: Phishing attack, Machine Learning, Random Forest, Feature Classification, URL.
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DOI:
10.17148/IJARCCE.2022.11695
