Web Extension for Phishing URL Identification

Phishing attacks have been the most consistent and emerging cyber-attack among other social engineering attacks. In spite of several strict mechanisms and policies to identify and avoid phishing websites, new techniques and tools are used by the attackers to exploit the security measures. This paper proposes a system that uses machine learning algorithm to identify phishing URL based on web parameters. Among several machine learning algorithms, Random Forest algorithm is chosen as it provides more accuracy and performance for the identification process. This system is implemented as a web extension in order to increase the performance of the identification process and to decrease the overhead of the system caused when using a standalone software.