URL Phishing attack Detection using Machine Learning Algorithms
M Karthick Kumar, N. Sivakumar · 2024
The pervasive integration of the Internet into our daily lives has made it an essential aspect; however, it has also created opportunities for malicious activities, such as Phishing. Perpetrators employ deceptive strategies, utilizing social engineering and mockup URLs, to surreptitiously obtain sensitive information such as account IDs, usernames, and passwords from individuals and organizations. Despite numerous proposed methods for detecting phishing websites, perpetrators adapt continuously, evading conventional detection techniques. Machine Learning emerges as a robust approach in identifying these malicious activities due to the shared characteristics inherent in most Phishing attacks. This paper conducts a comparative analysis of various machine learning methods utilized in predicting phishing websites, emphasizing the efficacy of these techniques in addressing cybercrime.