ANALYZING KEY COMPONENTS OF MACHINE LEARNINGBASED CYBERSECURITY AWARENESS PROGRAMS IN TELECOMMUNICATION NETWORKS

Abstract

This study explores the key constituents of machine learning-based program of cybersecurity awareness programs in telecommunication networks. Increasing complexity of cyber threats and attacks in the telecommunications sector has rendered traditional means of cybersecurity training insufficient to monitor dynamic security issues. This study looks at the possibility of machine learning models like Graph Attention Networks (GATs) to bolster cybersecurity awareness programs. The research is of a quantitative nature as survey responses have been used to gauge the efficiency of training modules driven by machine learning algorithms. The collected data was analysed statistically using statistical packages such as the Statistical Package for the Social Sciences (SPSS) as ANOVA to test the impacts of varied training methods on the knowledge and behavior of the participants in relation to cybersecurity. The research finds that programs propounded by machine learning project much higher performance than classical approaches in engagement, retention, and applicability of real-world cybersecurity. Further, the research indicates the usability of GATs in modeling user behavior and in adjusting learning in real time. This research contributes substantially to the development of more consequential and adaptive cybersecurity training solutions. The consequences of these findings are that the introduction of the machine learning into cybersecurity awareness training could lead to more scalable and resilient training solutions. This work emphasizes the need for a constant need for innovation in cybersecurity education to keep up with emerging threats and technologies