A machine learning approach improving university campus security

This paper aims to examine the existing vulnerabilities in the cybersecurity of a university campus and the approaches and ways to prevent losses caused by wrong decisions due to the analysis of corrupted data. The data analysis is usually performed using machine learning (ML) algorithms and artificial intelligence (AI). An approach to avoiding AI and ML vulnerabilities is presented in this paper. Ways to prevent wrong conclusions in data analysis and to avoid making wrong decisions are discussed. Another important aspect of improving security is ensuring a high level of cyber security and preventing (hacker) attacks, which can greatly endanger information processes and communication in the university and can also lead to the occurrence of material damage and human victims. Examples are provided to show how massive cyberattacks are in higher schools, based on data from universities in the United States of America. Several methods of attack prevention and detection are presented from the perspective of machine learning applications.