ConGISATA: A Framework for Continuous Gamified Information Security Awareness Training and Assessment

2026-04-16

AI Summary

This study addresses the critical issue of weak security awareness stemming from users’ lack of proactive risk perception regarding social engineering attacks. To tackle this challenge, the authors propose a mobile sensor–based, gamified framework for continuous security awareness training and assessment. The framework innovatively renders passive risks explicit by leveraging embedded sensors to capture real-world user behaviors, integrating gamification mechanics with behavioral simulation to enable progressive intervention and feedback. Experimental results demonstrate that the proposed approach significantly enhances users’ security awareness and behavioral responses in representative attack scenarios, thereby validating its effectiveness in supporting dynamic risk assessment and fostering proactive defense capabilities.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The incidence of cybersecurity attacks utilizing social engineering techniques has increased. Such attacks exploit the fact that in every secure system, there is at least one individual with the means to access sensitive information. Since it is easier to deceive a person than it is to bypass the defense mechanisms in place, these types of attacks have gained popularity. This situation is exacerbated by the fact that people are more likely to take risks in their passive form, i.e., risks that arise due to the failure to perform an action. Passive risk has been identified as a significant threat to cybersecurity. To address these threats, there is a need to strengthen individuals' information security awareness (ISA). Therefore, we developed ConGISATA - a continuous gamified ISA training and assessment framework based on embedded mobile sensors; a taxonomy for evaluating mobile users' security awareness served as the basis for the sensors' design. ConGISATA's continuous and gradual training process enables users to learn from their real-life mistakes and adapt their behavior accordingly. ConGISATA aims to transform passive risk situations (as perceived by an individual) into active risk situations, as people tend to underestimate the potential impact of passive risks. Our evaluation of the proposed framework demonstrates its ability to improve individuals' ISA, as assessed by the sensors and in simulations of common attack vectors.

Problem

• social engineering
• passive risk
• information security awareness
• cybersecurity
• human factor

Innovation

• gamified training
• continuous assessment
• mobile sensors
• passive risk
• information security awareness