Collaboration is not Evil: A Systematic Look at Security Research for Industrial Use

Abstract

Following the recent Internet of Things-induced trends on digitization in general, industrial applications will further evolve as well. With a focus on the domains of manufacturing and production, the Internet of Production pursues the vision of a digitized, globally interconnected, yet secure environment by establishing a distributed knowledge base. Background. As part of our collaborative research of advancing the scope of industrial applications through cybersecurity and privacy, we identified a set of common challenges and pitfalls that surface in such applied interdisciplinary collaborations. Aim. Our goal with this paper is to support researchers in the emerging field of cybersecurity in industrial settings by formalizing our experiences as reference for other research efforts, in industry and academia alike. Method. Based on our experience, we derived a process cycle of performing such interdisciplinary research, from the initial idea to the eventual dissemination and paper writing. This presented methodology strives to successfully bootstrap further research and to encourage further work in this emerging area. Results. Apart from our newly proposed process cycle, we report on our experiences and conduct a case study applying this methodology, raising awareness for challenges in cybersecurity research for industrial applications. We further detail the interplay between our process cycle and the data lifecycle in applied research data management. Finally, we augment our discussion with an industrial as well as an academic view on this research area and highlight that both areas still have to overcome significant challenges to sustainably and securely advance industrial applications. Conclusions. With our proposed process cycle for interdisciplinary research in the intersection of cybersecurity and industrial application, we provide a foundation for further research. We look forward to promising research initiatives, projects, and directions that emerge based on our methodological work.

[SoK] Evaluations in Industrial Intrusion Detection Research

“…In that regard, new evaluation methodologies, such as pragmatic assessments for machine learning-based network intrusion detection systems, are already being developed [8]. Lastly, a continuous exchange between all stakeholders should be established [63], e.g., in the form of public talks, workshops, or the dissemination of scientific publications. Only then can industrial operators stay informed about recent advancements and likewise keep dataset creators updated to ensure overall research strives for practical applicability.…”

“…1). To ensure the practical relevance of datasets, they should ideally be generated in close cooperation with industrial partners [63] since otherwise, IIDSs designed upon them risk not being of practical use to industrial operators. Such collaborations, even though costly [9], also allow enriching datasets with properties and demands of actual industrial deployments, e.g., the criticality of an attack, an acceptable delay until which a detection is excepted, or documentation of how long the ICS behaves abnormally after an attack until it stabilized again.…”