# Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling--An Oil and Gas Sector Case Study
## 2 Background and Related Work
### Related Work  
[PERSON] et al. [30] present a detailed examination of threat modeling in the context of cyber-physical systems (CPS), with a specific focus on a microgrid system. The paper aims to explore and apply the STRIDE threat modeling technique to cyber-physical systems, using a microgrid system as a case study. The study identifies a range of potential threats to the microgrid by creating a system attack taxonomy and highlighting key vulnerabilities in the system, such as weaknesses in communication protocols, inadequate access controls, and potential points of failure in system integration. The paper suggests that existing threat modeling techniques need improvement to better address the complexities of cyber-physical systems, including developing robust security measures, improving system resilience, and integrating threat modeling with other security practices to safeguard against identified threats. [PERSON] et al. [31] provide a comprehensive analysis of existing research on threat modeling methodologies specific to industrial control systems (ICS). The paper systematically reviews the literature on threat modeling techniques applied to ICS, aiming to identify trends, methodologies, and gaps in current research. The authors conducted a thorough review of academic papers, conference proceedings, and industry reports to gather and analyze information on threat modeling approaches used in the context of ICS and proposed an ICS threat modeling taxonomy to address specific vulnerabilities and threats inherent to ICS environments. The authors list a number of problems that come up when trying to model threats for ICS, such as the need for models that can change quickly to keep up with new threats and the need to include real-time threat intelligence. They also stress how important it is to keep improving threat modeling methods to make industrial control systems safer and more resistant to new threats. Recent research [32] reviewed the growing threat of cyber-attacks on industrial CPS in the offshore oil and gas industry as a result of technological advancements, the digitalization and integration of oil field equipment with corporate networks, and the need for remote monitoring and control. The study also looked at a typical subsea control system architecture and showed how vulnerable it is to MiTM, DoS, and spoofing attacks by connecting these types of attacks to one or more layers of the architecture. Those were linked to documented cybersecurity incidents affecting the upstream oil and gas industry. In line with one point of view in this paper about how important it is to protect critical manufacturing infrastructure (CPS) from high-level cyberattacks, [PERSON] et al. [33] created the first taxonomy-driven graph-theoretic model and framework to formally show this specific cybersecurity threat landscape and find manufacturing assets that are vulnerable and need immediate control. The proposed framework offers a critical step toward aiding researchers and practitioners in identifying the most critical connections and assets in a discrete manufacturing system. The proposed framework utilizes an attack graph formalism to integrate various threat attributes into the risk model, thereby enabling the simultaneous modeling and analysis of diverse cybersecurity threats with varying attack attributes. Moreover, attack graphs are used to model the cascading attack impact through different cyber and physical entities in manufacturing systems, leading to specific consequences. In addition, a quantitative model to estimate the cybersecurity risk associated with the identified attack paths, risk mitigation strategies, and recovery plans during threat events will be investigated using graph-theoretic tools. In FY 2016, the research team and [PERSON] evaluated three threat models (security cards, STRIDE, and persona non gata (PnG)). This project aimed to determine which of these methods was the most effective in identifying threats.