Abstract:
Phishing attacks represent one of the most prominent cybercrimes today, their main aim
is illicitly acquiring sensitive information such as passwords, user names, email bank
details, and credit card information. Their impacts extend across various sectors,
including online payment platforms, financial institutions, and cloud storage providers.
Usually, phishing attacks target websites associated with online payments and webmail
services. Various techniques have been used to combat phishing attacks, including
blacklisting, heuristic analysis, visual similarity checks, and machine learning. while
blacklisting is usually used to ease implementation, it falls short in detecting new
phishing attacks. Machine learning emerged as a highly efficient technique for detecting
phishing attacks, comprehensively addressing the limitations of other methods. This
research focuses on machine learning algorithms, namely logistic regression, decision
trees, random forests, and support vector machines (SVM)for phishing URL detection.
