This project focuses on the trade-off between the concept of cyber-psychology among the Nigerian Gen Z’s attitude that involves exploitation of cyberspace users and super smart society 5.0 subset features like Machine Learning Algorithms to combat network intrusion. A survey using google form questionnaire was taken as a sample at the Maranatha University, Lagos campus among the undergraduates of about 200 students. Apparently, the survey depicts Gen Z predominately depending on cyberspace as means of living. To further analyse the detection of cyberattack on the cyberspace which this age group mainly rely on. At the cross road of approaches to detect network intrusion, using Machine Learning techniques serves as the renaissance through which simulation of network scenario using Network Traffic Data for Intrusion Detection dataset which was implemented with the use of Waikato Explorer Knowledge Analysis (WEKA) as a data mining tool to build Machine Learning models like Naïve Bayes, J48, Random Forest and AdaboostM1. The best model in the experiment was Random Forest with evaluation metrics of precision, accuracy, Root Relative Squared Error (RRSE) and sensitivity as 1.00,0.985,0.389 and 1.00 respectively. The outcome of the simulation shows perfection of the Random Forest model to predict intrusion as cyberattacks after considering the independent variables of the dataset. The real-life scenario further suggests the need for Gen Z to substitute their cyber-psychology curiosity with Machine Leaning techniques as a perception to curb cybercrime.AI-driven driven cybersecurity is the future, which is undoubtedly needed by the Gen Z to leverage the benefits of the society 5.0 epoch.
ADEYEMO et al. (2026) studied this question.