ConGISATA: A Framework for Continuous Gamified Information Security Awareness Training and Assessment
Ofir Cohen, Ron Bitton, Asaf Shabtai, Rami Puzis
TLDR
ConGISATA is a gamified framework using mobile sensors for continuous information security awareness training, turning passive risks into active ones.
Key contributions
- Introduces ConGISATA, a continuous gamified framework for Information Security Awareness (ISA) training.
- Utilizes embedded mobile sensors, designed with a security awareness taxonomy, for assessment.
- Facilitates learning from real-life mistakes to adapt user behavior over time.
- Aims to convert passive security risks into active ones, improving user perception.
Why it matters
Cybersecurity is increasingly vulnerable to social engineering and passive risks that exploit human behavior. ConGISATA offers a novel, continuous gamified approach using mobile sensors to enhance individual information security awareness. This helps users actively recognize and mitigate risks, strengthening defenses against human-centric attacks.
Original 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.
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