# Embedding Security Awareness in IoT Systems: A Framework for Providing Change Impact Insights
## 2 Background
SACs are hierarchical and traceable structures composed of interrelated elements that collectively demonstrate the argument why a system can be considered secure, supported by both evidence and logical rationale. A SAC begins with high-level Goals that state the system's primary security claims or objectives. These are incrementally decomposed into Subgoals and Strategies, which outline how the claims will be achieved--often by pointing to specific components, mechanisms, or design decisions. Solutions represent evidence or artifacts (e.g., test results, certificates) that support the claims. Additional elements, such as Context, Justification, and Assumptions, provide relevant environmental information, rationale for decomposition choices, and any underlying assumptions of the argument, respectively. By tracing SAC, stakeholders can clearly understand how the system satisfies its security requirements and identify dependencies between functional and security objectives, associated controls, and the supporting evidence [16; 21]. GSN offers a graphical representation of SACs, using standardized shapes and links: Goals and Subgoals are rectangles, Strategies are parallelograms, Solutions are circles, and Contexts are ellipses. These elements are connected using SupportedBy (solid arrow) and InContextOf (hollow arrow) relationships [22]. A SAC template built on GSN, such as the one proposed in [16], formalizes security claims and control statements, providing a robust foundation for modeling a system's security profile. This structured abstraction is a crucial step toward embedding _security awareness_ into autonomous IoT systems. Nevertheless, effective _security awareness_ requires more than just interpreting how uncertainty impacts the system's security profile--it also demands an understanding of how these changes influence overall system functionality. Mapping XAI-generated insights into SAC structures helps bridge the interpretability gap by aligning model decisions with defined system behaviors and security objectives [6]. This alignment strengthens the precision of component-level impact assessments and provides a clearer picture of the system's evolving security posture. However, merely identifying which components are affected offers a limited view. It is equally important to assess the operational significance of those components within the system. Node centrality measures--widely used in network flow analysis to estimate the importance of nodes--offer a quantitative means to evaluate component criticality [25; 27]. To apply these metrics, the system architecture can be modeled as a directed graph using a call graph representation [23; 24], which captures process-level interactions between components. Centrality metrics such as Degree Centrality and PageRank [25; 26; 27; 28] can then be employed to evaluate each component's influence and priority in sustaining system operations. Real-world IoT systems typically comprise a large number of components whose interactions dynamically evolve due to contextual changes--components may be activated or deactivated depending on operational needs. Thus, estimating component significance must account for these dynamic shifts and continuously update assessments of criticality. Researchers [48; 49] have recommended the PageRank algorithm as a powerful method for identifying important nodes in evolving graphs. While traditional centrality measures [50] such as Degree, Closeness, and Betweenness Centrality evaluate importance based on direct or shortest-path connections between nodes, the PageRank algorithm leverages the concept of random walks to capture more nuanced influence patterns. This makes it particularly effective for uncovering cascading effects that may result from the failure of a single component, illuminating how disruptions can propagate through the system.