Abstract
Cybersecurity (CS) is a contemporary field for research and applied study of a range of aspects from across multiple disciplines. A cybersecurity expert has an in-depth knowledge of technology but is often also recognized for the ability to view technology in a non-standard way. This paper explores how CS specialists are both a combination of professional computing-based skills and genetically encoded traits. Almost every human behavioral trait is a result of many genome variants in action altogether with environmental factors. The review focuses on contextualizing the behavior genetics aspects in the application of cybersecurity. It reconsiders methods that help to identify aspects of human behavior from the genetic information. And stress is an illustrative factor to start the discussion within the community on what methodology should be used in an ethical way to approach those questions. CS positions are considered stressful due to the complexity of the domain and the social impact it can have in cases of failure. An individual risk profile could be created combining known genome variants linked to a trait of particular behavior using a special biostatistical approach such as a polygenic score. These revised advancements bring challenging possibilities in the applications of human behavior genetics and CS.
Highlights
Cybersecurity (CS) is a contemporary field for research and applied study of a range of aspects from across multiple disciplines
This paper describes how human factors could be identified from the genetic data and used for personalized risk assessment taking stress as a case
Integrating human behavior factors identified from genomic data into risk assessment strategies and professional training outside the standard IT-oriented training schema is a thrilling challenge but with great additional value
Summary
Cybersecurity (CS) is a contemporary field for research and applied study of a range of aspects from across multiple disciplines. Recent technological developments in approaches, i.e., machine learning algorithm approaches, have aided in the analysis of phenome-wide association studies (PheWAS), expression quantitative trait loci (eQTL) analysis, or whole-exome, whole-genome analysis. These advancements bring challenging possibilities in the applications of human genetics and CS. An individual risk profile could be created combining known genome variants linked to a trait of particular behavior using a special biostatistical approach such as a polygenic score With this knowledge, a CS specialist could become more aware of personal characteristics and environmental conditions and learn to mitigate potential threats. We discuss the complex matter which was, and still is, the main limiting factor of behavior genetics applications—the complexity of behavioral traits, methodologies, and technologies dedicated to the research of genetic architecture of a trait, ethical aspects, and competence frameworks
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