# Threat Modelling and Beyond-Novel Approaches to Cyber Secure the Smart Energy System
## 2 Related Work
### Stochastic Modeling of Cyber Attacks in Smart Grid Systems  
In the context of cyber security, stochastic models are used to estimate the effect of the behavior of an attacker. For this sake, they analyze probability distributions of specific variables for a vulnerability. On the other hand, risk assessment is applied in order to identify existing threats, vulnerabilities or potential risks from potential consequences, as given by [PERSON] et al. [10]. [PERSON] et al. [18] performs a cyber security risk assessment for attacks in SGs. For this matter, they methodically analyze the impact and likelihood of cyber attacks against such systems. Therefore, they implemented the toolbox Smart Grid Information Security (SGIS), which is applied for the estimation of risks for information assets. The toolbox is applied to two use cases, namely, voltage control and power flow optimization. The authors possible consequences for SGs due to exploitation of information assets. The paper by [PERSON] et al. [19] introduces a generalized model-based approach to assess realistic security risks for failure scenarios in SGs. The authors apply an assessment tool to analyze several of such scenarios. Each scenario is formalized in a structured model, which contains information about concrete vulnerabilities and attacker characteristics. These models are used in order to reason about failures in every scenario. The authors emphasize the advantage of their tool, namely the re-usability of generated models for different attacker profiles and systems. [PERSON] [20] provides a thorough theoretical analysis on risk assessment and failure scenario ranking in the electric sector. The work analyzes vulnerabilities as well as the resulting consequences and possible mitigation mechanisms. These vulnerabilities are listed and suggestions are given to define their naming conventions and classification. In fact, this work couples failure scenarios with security controls from [21]. [PERSON] and [PERSON] [22] proposes a cloud-based solution for fault location estimation in grid systems. For this matter, the approach relies on a weighted least-squares (WLS) method, which is used to model a fault location estimation problem as a non-linear estimation problem. The authors observe that the probability of inaccuracy estimation increases as a consequence of multiple measurements. However, since the approach detects erroneous measurements, the overall estimation error remains below 1% at different points in the SG system. The work from [PERSON] et al. [23] introduces game-theoretic models for the estimation of attack probabilities. For this sake, the authors analyze the interactions between attacker and defender in a grid network. Then, they apply game theory in order to implement a probabilistic model. This represents a systematic Boolean attack-defense model, thereby analyzing the overall infrastructure in a top-down manner. In their example, in authors assume that the attacker obtained information about the target system prior to the attack. In this way, the probabilistic model returns success rates for both the attacker and defender. In the attack scenario, both parties assume certain action after calculating the opponent's success rate. However, in order to reproduce this approach, broad knowledge about the capabilities of the infrastructure is necessary, which is provided by facility users. For both sides to draft a probabilistic strategy, knowledge about incidental degradation, incurred costs, defender strategies and defense priorities is mandatory. The approach [PERSON] et al. [24] applies a Monte Carlo method, which is based on Markov chains for operation reliability in power systems. For this sake, the authors focus on time-dependent state probabilities for state prediction. In this method, a system state is defined in terms of a specific situation of its composite states. In this case, the probability of each state is depicted with corresponding state models.