## 4 Proposed Exploratory Cyber-Physical Safety Analyzer Framework  
The AI-enabled exploratory cyber-physical safety analyzer framework for civilian UAM operations is depicted in Figure 2. In this framework, we devise a modular design by utilizing the concept of an AI pipeline. The proposed framework first analyzes the historical UAM operational data for feature selection, finding a correlation and missing value imputation. For example, if we consider UAM attitude control, then we need to analyze the sensor measurements and their changes, since jamming or spoofing attacks may change the behavior of a UAV's attitude operation. This sensor observation includes longitudinal and roll, transverse and pitch, and vertical and yaw motions to predict the abnormal behavior of a UAV.  
In this framework, we can train several AI models based on the requirements of the UAM security analyzer. The AI models are trained based on the principle of existing algorithms in a supervised manner. Thus, in this work, we have developed decision tree [9], random forest [10], logistic regression [11], K-nearest neighbors (KNN) [12], and LSTM [13] schemes on top of the proposed framework for justifying the performance of the proposed cyber-physical safety analyzer framework. Furthermore, the trained models are saved for real-time deployment in a security analyzer to protect commercial UAVs from cyberattacks and theft  
Figure 2: Modular view of the proposed artificial intelligence-enabled exploratory cyber-physical safety analyzer framework for civilian urban air mobility operations.  
The security analyzer utilizes a training model for ensuring cyber-physical safety for commercial UAM. In particular, based on the analysis of real-time UAM operational data through the trained AI model, the security analyzer can predict and find the cause of security threats by deploying the Pearson's correlation coefficient [15]. The proposed framework is trained offline via historical operational data and executed in real time by using a trained model and current UAM operational data.  
The training and execution procedure of the proposed AI-enabled exploratory cyber-physical safety analyzer is given as follows:  
* Offline training (red rectangle in Figure 2) 1. Collect historical UAM operational data; 2. Preprocessing historical datasets for feature selection and missing value interpretation through averaging; 3. Deploying AI algorithms such as decision trees [9], random forests [10], logistic regression [11], K-nearest neighbors (KNN) [12], and long short-term memory (LSTM) [13] for security analyzer model training using Equation (1); 4. Save the trained models.
* Real-time execution and analyzing threats (green-dashed rectangle in Figure 2) 1. Load the trained model and use real-time UAM operational data for predicting cyber threats such as jamming and spoofing; 2. Based on the outcome of the AI model, execute the [PERSON]'s correlation coefficient [15] matrix in Equation (3) for analyzing the findings for UAM safety; 3. Take necessary actions using the findings.  
Offline training and real-time execution of the proposed AI-enabled exploratory cyber-physical safety framework were physically deployed in a centered controller of a commercial UAM infrastructure. We developed the proposed exploratory cyber-physical safety framework in the Python platform [26]. A detailed description of the experiment is discussed in the next section.