# A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks
## 2 Related Work
### Smart Grid  
One of the early studies that provided a way to analyze the impact of CPA on the power system was by [PERSON] et al. [7]. The authors presented the idea of a \"cyber-to-physical\" bridge, which shows how attacks from the cyber domain propagate to the power domain. The bridge consists of four components: (1) attack vectors, (2) outcome of attacks, (3) technical effect on the grid and (4) grid impacts. The paper estimates the grid performance degradation by quantifying the grid reliability. The reliability is measured using various indices: Frequency Of Interruption (FOI), Loss Of Load Expectancy (LOLE), etc. The weakness of this approach is that it does not consider the dynamic nature of cyber-attacks and does not explain how attacks originate. [PERSON] et al. (2015) demonstrated integrity attack on a chemical reactor to show how cyber-attacks impact a CPS. Through contingency simulations, they understood the behavior of a chemical reactor in the presence of attacks, and they described the attack detection methods and response to attacks. [PERSON] et al. (2016); [PERSON] et al. (2016) emphasized the fact that SG is dependent on the cyber domain for monitoring and controlling, which broaden the surface area of attack. The paper provides details of the wide variety of attacks (integrity, replay, DDoS, etc.) and what their impact is on the SG. [PERSON] et al. (2016); [PERSON] et al. (2016) demonstrated the consequences of the load drop attack on the SGR. Through the combination of simulations in PowerWorld and Network Simulator, the authors showed how compromising the end nodes over Advanced Metering Infrastructure (AMI) of the SG to perform load drop attack destabilizes the underlying power grid. The parameter Time To Criticality is identified through simulations, and engineers should consider this while developing more resilient and robust algorithms for SG. Similarly, the authors in [PERSON] et al. (2016) evaluated the resilience of the power grid by sending on/off commands (in order to drop (off) or increase (on) load) to a certain percentage of high wattage IoT devices (compromised as a part of a huge IoT botnet) such as air conditioners, electric heaters, etc. The authors in [PERSON] et al. (2016) performed a detailed security analysis of the AMI to show different attack surfaces corresponding to various AMI components. In [PERSON] et al. (2016), the authors performed packet replay attack against the authentication scheme over the AMI network. The packet replay attack drains the resources, similar to DDoS attacks, of the system. The results of the study show that there is an increase in authentication time due to replay attacks. The authors in [PERSON] et al. (2016) modeled the propagation of a worm using the Weibull distribution over an AMI network. The Weibull distribution models the time needed for a smart meter in AMI to transit from a normal to malicious state. Through simulations in the Network Simulator, two parameters were identified that affect how fast the worm propagates within an AMI. First is the transmission range of meters, and second is the size of the worm. Finally, the authors discussed the physical consequences of the worm propagation in the power grid. The authors in [PERSON] et al. (2016) proposed a distributed non-linear controller to improve the transient stability of synchronous generators in the presence of excessive communication delay (over AMI) and cyber-physical disturbances. The concept of defining resilience using a Bayesian Network (BN) has frequently been used in securing CPS such as in [PERSON] et al. (2014); [PERSON] et al. (2015); [PERSON] et al. (2016); [PERSON] et al. (2016). [PERSON] et al. (2016) described a three-layer framework (static, dynamic and behavioral) that analyzes the risks introduced by the apps in the Android system. The authors modeled the system as a Bayesian risk graph. Similarly, in [PERSON] et al. (2016), the authors have modeled IT infrastructure that assists administrators to compute the chances of compromise.