Research on Anomalous Behavior Detection and Dangerousness Assessment among Civil Aviation Passengers at Checkpoints

Traditional civil aviation security check measures are focused on baggage rather than passengers. The goal of this study is to enhance the level and effectiveness of security measures. We propose an anomalous behavior detection technique for civil aviation passengers and a passenger risk-assessment method based on a neural network method. A large number of real cases were analyzed and summarized to extract indicators of anomalous behavior of civil aviation passengers, and an index system was developed to detect anomalous behavior of passengers at checkpoints. A neural network method was used to evaluate the passengers and classify the risk level to detect potentially dangerous personnel, monitor people, and create an emergency warning system. The synthetic minority oversampling technique (SMOTE), the conjugate gradient method, and a multilayer perceptron neural network were used to classify the risk level of passengers at checkpoints. The results demonstrated that the proposed index system and evaluation method were well suited to deal with the ambiguity and uncertainty in the recognition process. The anomalous behavior of civil aviation passengers at checkpoints and the associated threat level were accurately identified.