## 6 Issues and Challenges in the Defense Against Deliberate Attacks on IECPS
### Complexity of Modeling and Solving  
The difficulty in modeling deliberate attacks in IECPS mainly arises from the diversity of equipment types, significant differences in energy flow, and the heavy influence of uncertainty. IECPS includes numerous defense components and flexible devices (i.e., remote switches, EVs, combined heat and power, power-to-gas devices, etc. ), and there are complex interrelationships between these components and devices. Furthermore, due to differences in layout or system configurations, even the same type of device may exhibit different responses when facing deliberate attacks. Additionally, with the involvement of cyberattacks, modeling must also account for the cyber network and the cyber-physical coupling mechanisms, further increasing the complexity of the device models. Figure 18: Comprehensive performance curves for energy system operation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator Categories** & **Indicators** & **Evaluation Object** & **Ref. ** \\ \hline \multirow{7}{*}{System structure-based indicators} & Node degree, node betweenness, & \multirow{2}{*}{Power system} & [222] \\  & weighted node degree of betweenness, & & \\  & connectivity & & \\ \cline{1-1}  & Distribution line strength & \multirow{2}{*}{Distribution network} & [223] \\  & Number of common branches, number & & \\ \cline{1-1}  & of switch operations, path redundancy & \multirow{2}{*}{Power system} & [224] \\  & ratio, equipment availability & & \\  & Component redundancy & Integrated energy system & [225] \\ \hline \multirow{7}{*}{System performance-based indicators} & Load loss expectation & Integrated energy system & [219] \\  & Load recovery rate & Power System & [226] \\ \cline{1-1}  & Performance curve integral area & Power system & [227] \\ \cline  
Secondly, due to the significant dynamic differences in energy flow among different subsystems, the impact of implementing deliberate attacks and defense strategies in different systems exhibits complex spatiotemporal correlations. It is not feasible to simply understand IECPS as merely the sum of multiple subsystems, nor can the system model be simplified from a holistic analysis perspective. Only by combining multi-timescale methods to establish a dynamic operation model for IECPS can this spatiotemporal correlation be described. Compared to steady-state system models, the difficulty of modeling increases dramatically. For complex systems like IECPS, it is challenging to comprehensively model and efficiently solve the system state using traditional model-driven methods alone. Data-driven AI methods can effectively address the challenges of complex system modeling and solving by extracting hidden information and knowledge from vast amounts of data, enabling autonomous learning and automatic modeling. However, AI-based modeling solutions face the challenge of lacking interpretability. At present, the problems of system modeling and solving affect the whole process of deliberate attack and defense. In the pre-prevention strategy with planning as the core, the strategy has low requirements for the model's solution speed. However, the accuracy of the model itself and the accuracy of the solution method seriously affect the cost investment in the planning phase. In the case of limited defense costs, it may lead to a weaker defense system in the configuration. In the mid-event response phase, the accuracy and solution speed of the model have the greatest impact. First of all, the detection of deliberate attacks depends heavily on the accurate grasp of the system state, and models with large deviations are easy to cause missed detections and false detections. Secondly, the formulation and implementation of fault isolation and multi-source collaborative scheduling strategies are highly time-sensitive. Attackers can exploit this vulnerability to carry out multi-stage attacks and further expand the scope of the attack. Therefore, it is necessary to put forward higher requirements for the solution accuracy and speed of the model.