Detection of Phishing URLs Based on Machine Learning and Cybersecurity
Aya El-Sayed El-Metwaly, Mohamed Reda Bedair, Saleh Tamer Abdallah, Abdelrahman Mohammed Mahmoud, Mohamed Eid Mohamed, Mahmoud Elsherbiny Mahmoud, Ali E. Takieldeen · 2024
Phishing attacks represent a significant threat in the digital landscape, leveraging deceptive tactics to pilfer sensitive information from unsuspecting individuals, including usernames, passwords, and financial details. With the rise of such malicious activities, cybersecurity experts are tirelessly endeavoring to develop robust detection mechanisms tailored to phishing websites. This paper delves into the realm of machine learning, specifically focusing on the utilization of Decision Trees, Random Forests, and Support Vector Machine algorithms for identifying phishing URLs. By scrutinizing accuracy rates, false positives, and false negatives across these algorithms, the objective is to pinpoint the most effective machine learning approach for combating the ever-evolving menace of phishing attacks. Keywords: Phishing attacks, Detection phishing, Machine Learning Techniques, Social Engineering, URL