A Systematic Review of a Deep Learning Algorithm for Phishing Attack Detection

Abstract

The most common kind of cybercrime now is phishing, it attempts to deceive users into divulging sensitive information like passwords, bank details, and account numbers. Such cyberattacks frequently take advantage of electronic communication channels such as emails, instant messaging, and phone calls. Nowadays, there has been a dramatic uptick in the building of computer networks. Looking at the present pattern among people who use computers worldwide, it is evident that they are required to establish a connection between their PCs and the web. These results highlight the critical nature of Internet connectivity, whether for personal or professional reasons. However, users' privacy is at risk due to the widespread usage of this network, particularly for those users who do not activate security software on their computers. Once this vulnerability is exploited, hackers would be able to breach networks and launch attacks. Hackers may steal sensitive information, including login credentials to online accounts like banks and social media, making this a major concern for anybody using the internet. Some of the assaults that can be launched include phishing attempts. Reviewing the many forms of phishing attempts and the solutions now employed to avoid them is this research set out to do. Deep and machine learning has shown to be an effective tool in the fight against phishing, according to the study. It is possible to use a variety of approaches in the deep and machine learning approach to ward off such assaults.