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

TL;DR

ConGISATA is a continuous, gamified mobile sensor-based framework designed to enhance individual cybersecurity awareness by transforming passive risks into active ones through real-life learning and assessment.

Contribution

It introduces a novel mobile sensor-based gamified training framework with a taxonomy for evaluating security awareness, enabling continuous and adaptive learning.

Findings

Framework improves individuals' security awareness as shown by sensor data.

Simulation results demonstrate effectiveness against common attack vectors.

Continuous training leads to behavioral adaptation in real-life scenarios.

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.