Analysis of Social Engineering Security Challenges and Its Emerging Technologies

Information Technology is advancing at a record rate in the twenty-first century. It has grown to be an integral component of our daily lives. As the globe develops and more people use ubiquitous computing, cyber-security risks and privacy issues also grow proportionately. Cyber security is crucial to the information technology industry. Cyber-crimes, which are on the proliferate daily, are the first thing that transpire to mind whenever we think about cyber security. In spite of several safety measures, many people are still utterly concerned about cyber security. According to studies, social engineering attacks, which account for a sizable portion of all cyberattacks, are growing more common. By manipulating consumers’ minds, social engineers might trick them into revealing sensitive information or compromising their security. One of the best ways to defend against social engineering attacks is to be able to identify them. Phishing is a common method of social engineering attack that is used to start more than 90% of successful hacks and data breaches. Cyber-attack detection and prevention are challenging tasks. This paper provides an extensively analysis of social engineering attacks, including their categories and a pop-up phishing classification system that we developed uses one of the best-known classifiers Support Vector Machine of Supervised Learning Paradigm in Machine Learning and Wrapper-based feature selections that incorporates the metadata of URLs are used extracts elements intended to defeat popular phishing detection techniques in order to provide a security model and to make predictions. Furthermore, this study offers an overview of the problems with phishing attacks now as well as recommendations for future research in this area.