# Automatic Hybrid Attack Graph (AHAG) Generation for Complex Engineering Systems +
## 1 Introduction  
As a result of the rapid advancement of complex engineering systems such as infrastructure, communications, energy systems, industrial automation, artificial intelligence, and cyber-physical systems, new research directions in modeling, monitoring, diagnosis, optimization, and control have emerged in recent years [1]. For instance, a control chart scheme for production processes was developed by [2] for monitoring the mean time between two events under the neutrosophic statistics using the belief estimator for the neutrosophic gamma distribution. The diagnosis and correction of many production problems which often cause huge loss to the production unit can substantially be improved with the utilization of the effective control chart technique. In [3], the advantages of using Proportional-Integral (PI) controller for pH control in the raceway reactor during the whole day against traditional On/Off control were demonstrated. The paper also presented an event-based control architecture for Proportional-Integral-Derivative (PID) controllers. The objective is to tune a classical time-driven PI for pH control in the raceway reactor, and then to add event-based capabilities, but keeping the initial PI control design. The event-based systems allow a trade-off between control performance and control effort, which is perfect for the microalgae process in raceway reactors. The performed tests were oriented to establish a trade-off between control effort and control performance and present an alternative to traditional control. The applicability of the Distributed Model Predictive Control (DiMPC) was investigated in [4] to deal with the constraints in the steam/water loop of a steam power plant. A comparison was conducted between the Decentralized Model Predictive Control (DeMPC), the Centralized Model Predictive Control (CMPC), and the DiMPC. The results showed the effectiveness of the DiMPC [5] designed an Optimal Nonlinear Adaptive Control (ONAC) strategy to achieve optimal parametertuning of Nonlinear Adaptive Control (NAC) for Voltage Source Converter (VSC) operating in both rectifier mode and inverter mode where an optimal and robust control can be achieved under different operation scenarios. A novel pole-zero cancelation method was proposed by [6] for Multi-Input Multi-Output (MIMO) temperature control in heating process systems. In the proposed method, the temperature differences and the transient response of each point can be controlled by considering the dead time and the coupling effect of the MIMO system. In [7], the Slow-Mode-Based Control (SMBC) method combined with decoupling and dead-time compensation was applied to the MIMO temperature control system. The temperature differences and the transient response of all points can be controlled and improved by making the output of the fast modes follow that of the slow mode. The results were then compared to the conventional PI control and gradient temperature control methods. The Deep Deterministic Policy Gradient (DDPG) technique for the optimum boost control on a Variable Geometry Turbocharger (VGT)-equipped engine is implemented by [8]. The proposed DDPG algorithm is compared with a fine-tuned PID controller to validate its optimality. The results showed that the control performance based on the proposed DDPG algorithm can achieve a good transient control performance from scratch by autonomously learning the interaction with the environment, without relying on model supervision or complete environment models. A framework was proposed by [9] to use the structural information in each Possible Conflict (PC) for fault diagnosis of complex industrial system to design a different kind of executable model. They proposed to build grey-box models based on a state space neural network architecture derived from that structural information in the PC, which links measurements with equations, and consequently with parameters related to faulty behavior.