# Embedding Security Awareness in IoT Systems: A Framework for Providing Change Impact Insights
## 1 Introduction  
The rapid advancement of Internet of Things (IoT) technology has profoundly transformed modern life, reshaping how we interact with our surroundings and driving innovation across numerous sectors [1]. IoT systems are particularly effective at automating processes and optimizing resource utilization in areas such as smart homes, healthcare, agriculture, and industrial automation. By seamlessly interconnecting a wide range of devices--collectively referred to as \"things\"--over the internet, these systems are designed to sense, analyze, and intelligently respond to both operational and environmental conditions. Continuous monitoring of system attributes enables IoT devices to generate actionable insights, significantly enhancing task management and decision-making processes [1, 2, 3]. As IoT adoption continues to expand, the exponential increase in connected heterogeneous devices and their complex interactions introduces significant challenges, particularly in terms of security and system resilience. IoT ecosystems comprise a broad spectrum of components, ranging from resource-constrained sensors to high-performance cloud platforms, each characterized by distinct computational capabilities, functionalities, and communication protocols [4, 5]. This inherent heterogeneity results in divergent security requirements and protection mechanisms, which can create vulnerabilities exploitable byadversaries. The high degree of interconnectedness within these systems further amplifies the risk; a compromise in a single weak component or an unexpected environmental disturbance can cascade through the network, jeopardizing the overall system's security and functionality [4]. Moreover, suboptimal security practices by IoT users, coupled with the unpredictable nature of threats stemming from interconnected devices and services, further exacerbate these vulnerabilities. Conventional static, rule-based decision-making strategies are inadequate for addressing the dynamic and unpredictable nature of IoT environments. In contrast, effective dynamic decision-making requires the ability to accurately interpret changes in the operational environment, reason about their implications for system security and functionality, and adapt strategies accordingly. Researchers [6; 7; 8] emphasize that embedding _security awareness_ into IoT systems is essential to enable them to reason effectively about appropriate mitigation responses in the face of uncertainty. _Security awareness_, conceptualized as an extension of self-awareness, empowers IoT systems to detect changes in both environmental conditions and internal system attributes while concurrently assessing their implications for the system's overall security posture [6; 9; 10]. This capability involves the real-time extraction of security-relevant insights by analyzing variations in the system's behavior and operating context [6; 10]. A key challenge in embedding _security awareness_ lies in processing the large volumes of heterogeneous data generated in real time. In this regard, advanced machine learning (ML) and deep learning (DL) techniques, combined with powerful computational resources, have emerged as critical enablers, driving IoT systems toward greater levels of autonomy [11; 12; 13]. However, while ML/DL models excel at detecting and predicting changing conditions, data processing alone is insufficient to realize full _security awareness_. True _security awareness_ must go beyond detection by incorporating robust methods for interpreting the effects of uncertain or unforeseen changes on the system's security profile. This involves identifying the specific functionalities and security requirements affected by such changes and evaluating their significance within the broader application context [6; 14; 15]. Achieving this level of interpretability requires a formal articulation of the system's security profile, which includes abstract representations of its security requirements, operational constraints, and the interdependencies among internal components [16].