The emergence of fraudulent websites is a serious concern since it can defraud gullible Internet users of large amount of money. The intricate layout of these phony websites makes it difficult to identify them manually. Automated detection systems have become an essential defense mechanism. Nevertheless, many of these systems rely on crude fraud indicators, which are sometimes unable to keep up with the speed at which fraudulent websites are created and vanish. Different strategies are needed to handle this issue and identify fake websites more successfully. Out of all of these, the random forest classifier performs better than other techniques. Real-time detection is facilitated by integrating our classifier into a browser plugin, which promises an impressive true positive rate of 98.8%. This study presents website authenticity enhancement using cybersecurity features by examining the effectiveness of random forest algorithm to identify phony websites.