# Enhancing Cybersecurity Through Comprehensive Investigation of Data Flow-Based Attack Scenarios
## 3 Proposed Approach
### Methodology
#### 3.2.3 Impact Enumeration
### Comparison with Related Work

Our proposed approach builds upon and extends the existing approaches discussed above, particularly the studies by [PERSON] [44], [PERSON] et al. [45], and [PERSON] et al. [42]. In [44] the use of the STRIDE threat modeling approach was explored to identify potential security threats in smart homes and healthcare systems. While their work demonstrated the applicability of STRIDE in these domains, it did not provide the level of granularity and systematic analysis of attack scenarios that our approach offers. In contrast, our approach goes beyond simply identifying threats and provides a comprehensive impact list of CPS-specific attack types. This list serves as a foundation for generating detailed attack profiles that capture the source, destination, and depth of potential attacks. This level of granularity allows for a more thorough understanding of the attack surfaces and potential impact within cyber-physical systems. In [45], the authors also utilized the STRIDE threat modeling technique; however, their focus was on automating generating security tests within software systems. While their work aimed to improve the repeatability and reproducibility of security testing, it did not address the unique challenges of cyber-physical systems, such as the tight integration of cyber and physical components; our approach, on the other hand, is specifically tailored to the CPS domain, incorporating the STRIDE approach to generate a CPS-centric impact list. When combined with the systematic analysis of attack scenarios and their propagation through the system, this list provides a more comprehensive solution for identifying and mitigating threats in cyber-physical environments. Our data flow-based attack scenario analysis differs from existing approaches in several important aspects. Unlike many existing methods that focus on either attack surfaces or data flows, our approach integrates both, providing a more holistic view of the system's vulnerabilities. We extend the STRIDE approach with a detailed mapping of attack scenarios, addressing a limitation of the original STRIDE approach. In terms of existing methodologies, attack trees and attack graphs provide valuable frameworks for understanding potential attack paths; however, they often lack the detailed integration of data flow analysis that our approach provides. While the STRIDE approach is useful for enumerating threats, it does not provide the necessary depth in mapping attack sequences, which can leave critical systems vulnerable to sophisticated multistep attacks. Our research aims to fill this gap by combining STRIDE-based threat modeling with detailed data flow analysis and attack scenario mapping. Furthermore, the integration of an attack scenario API in our approach is a unique feature that sets it apart from related studies. This API generates and exposes potential attack chains, facilitating seamless integration with other security tools and processes. This standardized interface enhances the overall utility and applicability of our approach within the CPS security ecosystem. In contrast to the study published in [42], which focused on privacy threats and the LINDDUN framework, our approach specifically addresses security threats and attack scenarios in cyber-physical systems. While both approaches leverage the STRIDE methodology, our work is tailored to the unique challenges and vulnerabilities present in CPS contexts, providing a more targeted solution for this domain. In summary, our proposed approach represents a significant advancement in the field of attack scenario identification and analysis for cyber-physical systems. ## 8 Conclusions and Future Work

In this paper, we provide a comprehensive approach for the evolving landscape of industrial cybersecurity that emphasizes the early integration of cybersecurity measures. The increasing integration of the IoT requires careful monitoring of critical assets in cyber-physical systems.