# A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks
## 6 Risk Assessment Methodology  
The risk is a function of the impact of an event (e.g., cyber-physical attack) and the likelihood of that event (PoC) [40]. In CPS, the risk is measured in terms of the likelihood of cyber-physical attacks and their impact on the system. The likelihood of CPA depends on the vulnerabilities of the cyber system components that an attacker wants to exploit to mount sophisticated attacks. In order to perform the risk assessment of CPS, we need to compute the likelihood of CPA and analyze their impact. The following steps must be taken by the system admin of a CPS to perform the risk assessment:  
1. Define the cyber system that controls a specific functionality of the physical system, in the form of a directed acyclic graph. The graph represents the information flow from the start of the system to the main system (such as the energy control center in SG). 2. Define the entry points in the system from the Internet. 3. Perform the vulnerability assessment of all the cyber components present in the graph. 4. Compute the CVSS base score corresponding to all vulnerabilities present. 5. Compute the PoC of each cyber component using the CVSS score of vulnerabilities they possess. 6. Use the directed graph as a Bayesian graph to compute the causal dependency between the systems. The PoC of each system is now dependent on its parents as well. 7. The PoC of a cyber component of interest is computed in the Bayesian graph (BAGS tool). 8. Using the simulator corresponding to CPS (for instance, PowerWorld in SG), perform contingency simulations by manipulating the system on behalf of an adversary. 9. Quantify the impact of attacks on the system and discover various parameters that are important to system resilience (for instance, frequency boundaries in this case). 10. Combine the PoC of a cyber component and its impact on the physical domain to determine the risk to the system. In this paper, we presented the BAGS tool to compute the likelihood of an attack on various cyber systems of SG. Then, we assumed the attacker has compromised a cyber system from where  
Figure 11: Unconditional probability distributions when the remote code execution vulnerability is discovered in the billing engine server. The effect of such a change is propagated to other components. he/she controls a physical process. We perform attacks on behalf of the attacker on the physical system using the PowerWorld simulator to understand the behavior of the grid in the presence of attacks. The likelihood is PoC. We create a range of probabilities and give names to them [40] (see Table 2). Since we know the PoC of each function, we can see in which probability range they lie and specify the corresponding qualitative value of likelihood. The impact of attacks depends on whether cyber components can manipulate the frequency of the grid. To manipulate frequency, the system admin must identify which cyber components are responsible for controlling the power generation, distribution, dispatch, storage, smart meters, etc. After identifying components, they should decide which components have a direct or indirect impact. The system admin specifies these values based on his/her experience and contingency analysis of the grid. The impact of attacks (consequence) is represented in terms of the frequency domain [10] (restricted, continuous and optimal) (see Table 3), and accordingly, we specify the qualitative values. The optimal domain is a safe region of operation because of the required amount of load responded to, to bring the frequency to its normal level within the required time. The cyber functions such as billing engine and smart meter head server do not affect the power generation. The continuous domain is a safe region, but not the desired region of operation. The system is not able to curtail the required amount of load within the required time to bring the frequency back to the normal scenario (60 Hz). The system should take actions such as load shedding or increase generation via peaker plants to meet load.