Gamification Tools for Social Engineering Attack Awareness
Usharani Bhimavarapu · 2025
This research investigates the application of gamification elements with deep learning methods to improve cybersecurity training, in this case, awareness of social engineering attacks. A quasi-experimental approach was utilized, where 125 undergraduate students were assigned to game-learning and lecture-learning groups. The research utilized the Ant Colony Optimization (ACO) algorithm in feature selection, maximizing dataset quality for examination. A Bi-Stacked Gated Recurrent Unit (Bi-Stacked GRU) model was employed to individualize learning processes by monitoring interaction patterns and dynamically adapting training material. Rewards, challenges, and immediate feedback were incorporated into gamification components to infuse motivation into learners and support cybersecurity topics. Outcomes indicated that gamified, interactive learning significantly impacted comprehension and memorization of social engineering attack methods by participants against lecture-based training.