  A thorough and systematic understanding of different elements of cyberattacks
is essential for developing the necessary tools to prevent, detect, diagnose,
and mitigate cyberattacks in manufacturing systems. In response, researchers
have proposed several attack taxonomies as methods for recognizing and
categorizing various cyberattack attributes. However, those taxonomies cover
selected attack attributes depending on the research focus, sometimes
accompanied by inconsistent naming and definitions. These seemingly different
taxonomies often overlap and can complement each other to create a
comprehensive knowledge base of cyberattack attributes that is currently
missing in the literature. Additionally, there is a missing link from creating
structured knowledge by using a taxonomy to applying this structure for
cybersecurity tools development and aiding practitioners in using it. To tackle
these challenges, this article highlights how cyberattack taxonomies can be
used to better understand and characterize manufacturing cybersecurity threats.
It also reviews and analyzes current taxonomical classifications of
manufacturing cybersecurity threat attributes and countermeasures, as well as
the proliferation of the scope and coverage in existing taxonomies. As a
result, these taxonomies are compiled into a more comprehensive and consistent
meta-taxonomy for the smart manufacturing space. The resulting meta-taxonomy
provides a holistic analysis of current taxonomies and integrates them into a
unified structure. Based on this structure, this paper identifies gaps in
current attack taxonomies and provides directions for future improvements.
Finally, the paper introduces potential use cases for attack taxonomies in
smart manufacturing systems for assessing security threats and their associated
risks, devising risk mitigation strategies, and informing the application of
cybersecurity frameworks.
