Blockchain technology has been gaining great interest from a variety of industry sectors, including financial, food processing, and power and energy markets. Realizing the strength of blockchain technology beyond the successful application in the cryptocurrency arena, researchers have been evaluating and using blockchain for applications such as supply chain management, transactive industry (both financial and energy), system integrity, device cybersecurity, identity management, and much more. One of the unique elements of the blockchain technology that made it such a captivating technology to researchers is its plethora of features. Some of the features include smart contracts, cryptocurrency and tokenizing, immutable distributed ledger, cryptographic hashing, and digital signature. In addition, there are multiple types of blockchains, such as permissioned/private and permissionless/public, and various consensus models, such as proof-of-work, proof-of-authority, proof-of-burn, and proof-of-stake. Therefore, it is often nontrivial to determine if an application requires a blockchain. If so, what kind of blockchain and consensus is most appropriate? This article discusses the blockchain applicability framework (BAF), which was specifically designed with the purpose to answer those questions. The BAF is divided into five domains, 18 subdomains, and about 100 controls. It is designed to ingest detailed user requirements to perform a weighted evaluation that is built on mathematical constructs to determine the ideal combination of blockchain that is appropriate for an application. Along with the core logical formulation of the BAF, this article depicts the efficacy of the BAF through two use cases.

Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases.

Critical infrastructure has become a focal point of cyberattacks, as previously isolated operational technology networks that were once perceived to be air-gapped are becoming Internet-exposed through increased connectivity with informational technology networks. Recent adversarial tendencies have led to an increase in targeted cyberattacks against both industrial control systems (ICS) and building automation systems. Furthermore, the insufficient supply of a cyber workforce exacerbates the challenges for organizations to defend their systems. Game-based learning is gaining traction and studies have shown that it is an effective educational element. Training facility operators responsible for critical services can be achieved through gamification of security policies and controls. The Network Defense Training Game (NDTG) is a cybersecurity training platform that encompasses a series of cybersecurity events that the player must assess and react to throughout the scenario to defend the network by thwarting the adversary’s attack. The NDTG uses scenario narratives based on historical cyber incidents that affect ICS. It is designed to train facility owners and operators to evaluate their cybersecurity posture and to apply cybersecurity frameworks before and during the process of addressing cyber events and incidents. This study provides a detailed technical overview and design architecture of NDTG and demonstrates its capability in advancing the ICS cybersecurity workforce.

Cybersecurity vulnerability assessment tools, frameworks, methodologies, and processes are commonly used to understand the cybersecurity maturity and posture of a system or a facility. Although those tools are strictly developed based on standards defined by organizations such as the National Institute of Standards and Technology (NIST) and the U.S. Department of Energy, the majority of these tools and frameworks do not provide a platform to prioritize the requirements to reach a desired cybersecurity maturity. To address that challenge, we have been developing a framework and a software application called the cybersecurity vulnerability mitigation framework through empirical paradigm (CyFEr). The efficacy of CyFEr was evaluated by implementing it on the NIST cybersecurity framework (CSF). This paper provides a detailed architecture of CyFEr and demonstrates its application to CSF by testing against a real-world cyberattack that targeted industrial control systems in a critical infrastructure facility.