# A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks
## 6 Risk Assessment Methodology
The system components such as the smart meter and smart meter collector indirectly affect the frequency by performing load drop or manipulating the readings of the power consumptions if compromised. Finally, the restricted domain is not safe at all. The components such as ECC, OMS and MDM that are responsible for controlling the power generation and dispatch come under this category. Table 4 maps the likelihood and consequence values to the risk levels. There are three risk levels: {Extreme (E), High (H), Low (L)}. For each function, we know the PoC using the BAGS tool. Moreover, we know the impact on the grid for each function if it gets compromised. Therefore, using Table 4, we compute the risk level for each function. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Risk Levels** & \multicolumn{3}{c}{**Consequences**} \\ \hline
**Likelihood** & **Major** & **Minor** & **Insignificant** \\ \hline Almost Certain & E & E & H \\ Likely & E & E & H \\ Possible & E & E & L \\ Unlikely & E & H & L \\ Rare & E & H & L \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Likelihood. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Probability Ranges** \\ \hline Rare & 0.0–0.19 \\ Unlikely & 0.2–0.39 \\ Possible & 0.4–0.59 \\ Likely & 0.6–0.79 \\ Almost Certain & 0.8–1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Qualitative values of consequences. Table 5 represents the risk level corresponding to each function based on the PoC and the consequence on the grid. We use probabilities computed in the above table and assign the likelihood and consequence for each function. After referring to Table 4, we specify the risk level for each function. ECC is the main component that controls the power generation, distribution and power dispatch. If it gets compromised, the whole grid may get destabilized. Therefore, we put major as the consequence of an attack via ECC, and it has the extreme risk level. Similarly, since OMS is responsible for managing outage power and it makes the decision such as load curtailment and load shedding, OMS also has the major level of consequence and thus extreme risk level. Even if the PoC is rarer or unlikely, if SG suffers an unbearable impact on its frequency, it poses the extreme risk. The billing engine compromise will not affect the grid frequency. Therefore, it has an insignificant consequence, and thus the low-risk level. The results of this methodology help power engineers to understand which system component is most vulnerable and easily compromised and that will have a significant impact on the frequency (stability) of the system. By combining the vulnerabilities of the cyber system of SG and the attacks on the grid, we provide the complete study of risk analysis of the SG. The risk analysis assists power engineers to understand the system dynamics during contingency and discover the relationship between cyber and physical domain variables. The question now arises how to allocate resources in order to maximize the resilience of the system at minimum cost. We answer this question in the next section using the RL-BAGS tool, an extension of BAGS.