Cognitive security is the interception between cognitive science and artificial intelligence techniques used to protect institutions against cyberattacks. However, this field has not been addressed deeply in research. This study aims to define a Cognitive Cybersecurity Model by exploring fundamental concepts for applying cognitive sciences in cybersecurity. For achieving this, we developed exploratory research based on two steps: (1) a text mining process to identify main interest areas of research in the cybersecurity field and (2) a valuable review of the papers chosen in a systematic literature review that was carried out using PRISMA methodology. The model we propose tries to fill the gap in automatizing cognitive science without taking into account the users’ learning processes. Its definition is supported by the main findings of the literature review, as it leads to more in-depth future studies in this area.

Cyberattacks capitalize on human behaviors. The prevalence of cyberattacks surged during the COVID-19 pandemic, fueled by the increased interconnectivity of individuals on online platforms and shifts in their psychological dynamics due to the pandemic’s context. The enhancement of human factors becomes imperative in formulating a robust cybersecurity strategy against social engineering in the post-COVID-19 era and in anticipation of analogous pandemics. This study aims to propose a model for delineating strategies across various phases of cyberattacks, grounded in the cyber kill chain model, while also encompassing cognitive mechanisms for adaptive responses. This approach aims to cultivate defensive cognitive factors like resilience and self-efficacy. To achieve this objective, we conducted an exploratory study adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Subsequently, we pursued a descriptive and correlational study based on prevalent attacks during the pandemic. The intention was to pinpoint proactive factors conducive to the development of cognitive capabilities to counter cyberattacks. These insights could pave the way for the creation of training programs and technological solutions aimed at mitigating the impact of such cyberattacks.

The growth of the Internet of Things (IoT) has accelerated digital transformation processes in organizations and cities. However, it has also opened new security challenges due to the complexity and dynamism of these systems. The application of security risk analysis methodologies used to evaluate information technology (IT) systems have their limitations to qualitatively assess the security risks in IoT systems, due to the lack of historical data and the dynamic behavior of the solutions based on the IoT. The objective of this study is to propose a methodology for developing a security risk analysis using scenarios based on the risk factors of IoT devices. In order to manage the uncertainty due to the dynamics of IoT behaviors, we propose the use of Bayesian networks in conjunction with the Best Worst Method (BWM) for multi-criteria decision-making to obtain a quantitative security risk value.