SOCIAL ENGINEERING ATTACKS DETECTION APPROACH

Abstract

With the development of modern technologies, the Internet has become the key to the exchange of various information and communications. As a result, such an evolution has brought decentralized access to data and information through file sharing through platforms, 
in particular such as social networks, which are generally not sufficiently secure.
The work is devoted to the problem of social engineering cyberattacks. These are cyberattacks that manipulate users, forcing them to disclose confidential information, to perform certain actions that lead to breaking existing security systems. Such cyberattacks often exploit human psychology, trust, and lack of vigilance to gain unauthorized access to networks, systems, or data. Thus, the privacy of Internet users is always at risk. Among cyber security crimes, social engineering attacks are the most powerful tool used by criminals.
Large-scale social engineering cyberattacks can have far-reaching consequences beyond individual victims or organizations. For 
example, cyberattacks on critical infrastructure, government systems, or utilities can disrupt the provision of essential services, endanger public 
safety, or undermine public trust.
Many transnational corporations and companies, news agencies and even government institutions of entire countries became victims of 
such attacks. Criminals gain access to information by targeting individuals, but in most cases, their main target is organizations with which such individuals have certain ties.
The article presents an attempt to detect social engineering cyberattacks. Four machine learning algorithms ( decision tree , random forest , K- nearest neighbor , and extreme gradient boosting ). The analysis focuses on data collected from network hosts that can serve as indicators of potential social engineering cyberattacks. Empirical results demonstrated high detection accuracy.