# Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM
## 1 Introduction
In today's AI research field, deep learning methods, large language models, and various types of traditional AI applications have demonstrated their capabilities in numerous scenarios, but in the specific and complex application scenario of terminal area control systems, the ISSA-ELM model has unique advantages. Deep learning methods [15; 16] typically rely on large-scale data training and complex neural network architectures to learn data features and patterns. However, in the terminal area control system, the data are often highly complex, real-time, and uncertain; especially when the data samples are relatively limited or the data distribution is not balanced, it is difficult to accurately capture a variety of complex nonlinear relationships in the terminal area control system, which leads to limited prediction accuracy. Large language modeling [17] has made remarkable achievements in natural language processing, and is capable of understanding and generating human language texts. However, terminal area control systems mainly involve non-natural language information such as the dynamic data of air traffic, flight parameters, and control instructions. When dealing with such structured and numerical data, the architecture and training methods of big language models are not specifically targeted, and it is difficult to directly and effectively excavate the deep features and laws behind the data that are related to the safety posture of the control system. Traditional AI applications [18], such as some rule-based expert systems or simple machine learning models, are able to deal with some of the problems in terminal area control systems to a certain extent, but their limitations are also more obvious. Rule-based expert systems rely on pre-set rules, which make it difficult to cope with new and unknown situations arising in complex and changing control scenarios, and lack adaptivity and flexibility. In contrast, the ISSA-ELM model has significant advantages in the terminal area control system, which combines the advantages of the Improved Sparrow Search Algorithm (ISSA) and the Extreme Learning Machine (ELM). The ISSA can effectively optimize the parameters of the ELM to improve the learning efficiency and accuracy of the model, while the ELM itself is characterized by a fast training speed, which can quickly process the real-time data in the terminal area control system and respond to the security situation of the system in a timely manner. In the face of the complex nonlinear data relationships in the terminal area control system, the ISSA-ELM model, through its unique structure and algorithms, is able to better capture the characteristic information in the data, and accurately predict and evaluate the risk factors, such as altitude deviation, unstable approach, and airborne hazardous approach. Meanwhile, the ISSA-ELM model is relatively simple and efficient, and its demand for hardware resources is not as harsh as that of deep learning methods and large language models, so it can operate well on the basis of the existing hardware architecture of the terminal area control system, which provides a more feasible and effective solution to ensure the safe and stable operation of the terminal area control system. This study first establishes the operational safety situation indicator for terminal areas, taking into account the features of the terminal area control system's operations. We employ a system engineering method known as Process Failure Mode and Effects Analysis (PFMEA) to identify factors that could potentially impact the safety of operations in the terminal area [13]. We start by evaluating the risk. We use the 2-4 model of accident causation theory to passively detect risks from five years of unsafe event data from terminal areas, ensuring thorough identification [19]. In addition, experts, safety managers, and front-line air traffic controllers provided information through questionnaires that identified the final 19 secondary and three primary indicators.