## 2 Related Work
### Quantifying Security  
To further quantify the security of an integrated setting, we have to measure the security of the underlying components and afterwards calculate the total protection. One main methodology to measure the provided security for a system is the medieval castle model [45; 46; 47]. The system is considered as a castle with doors that the attacker tries to break through. The infiltration is successful, when the attacker passes the doors and reaches the protected treasure rooms (e.g., assets which the deploy defense mechanisms try to safeguard) in the inner layers of the castle/system. The difficulty of bypassing these doors and reach the inner treasure rooms designates the provided security level for this castle/system. A door represents a security mechanism with the resilience to assaults being assessed by related metrics. The overall method can evaluate the defense of static system instances. However, it is not directly applicable for dynamic system analysis. Other approaches include the attack surface metrics [48; 49; 50; 51; 52; 53] and multi-metric approaches [4; 54]. A literature review for attack surface studies is conducted in [48]. Such methodologies (e.g., [49; 50; 51; 52; 53]) take into account that the attacker will utilize the system's channels, methods, and data elements to infiltrate the system. The set of these elements at the entry/exit ports which could be exploited by attackers, define the system's attack surface. Quantitative methods assess the damage potential-effort, determining the surface's size. In [4; 54], multiply quantitative metrics are incorporated and capture the system's protection aspects. The various metrics take the form of vectors with each factor evaluates a specific property, such as Security, Privacy, Dependability, and/or Usability. The measured values are in the range of 0-100 (modelling no to optimal satisfaction) and easy the composition of several metrics as well their comparison. Thereby, meta-heuristic methods are applied to assess and visualize the offered parameters of the current system setting. Moving Target Defenses (MTDs) [55] constitute an active and offensive protection strategy where the system configuration and setting is changed, either periodically or as a response to an occurred event, in order to harden the analysis of the system by an attacker or to mitigate ongoing attacks. Such solutions can incorporate security metrics in order to better evaluate the protection level of each potential system state. The study in [56] implements MTDs by exploiting the attack surface metrics. The proposed framework measures the distance of the security surface between different system states. The goal is to administrate responses to attackers' probes, so as to induce an external view of the system that complies with specific desirable properties. Minimizing the cost for the defender is also considered the study in [57] presents a multi-metric-driven management framework for e-health digital environments. The method is applied in e-health settings for chronic diseases and utilizes three metric categories. Risk-driven security engineering and assurance metrics are established at deployment-time, providing an early evaluation on the effectiveness of the deployed security. Continuous security monitoring metrics get assigned at operational-time and support the security correctness assessment, improved systematization as well as traceability among the various metric outcomes and product requirements. Then, automated adaptive decision-making metrics can be estimated at operational-time and achieve higher quality security effectiveness understanding in operational security monitoring and future versioning of the examined setting. The framework performs continuous security monitoring along with metric-driven fully- or semi- automated security decisions. A first theoretical work for the measurement of security is conducted in [58]. The authors document a formal model for the representation and evaluation of security metrics and define a set of relevant metrics.