Airline Passenger Profiling Based on Fuzzy Deep Machine Learning

Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

I. Introduction

Terrorism has threatened the freedom to travel throughout the history of commercial aviation [1]. In the last few years, there is an increasing number of serious air disasters around the world, many of which have been confirmed or suspected to be associated with terrorist attacks. Generally, in-depth screening and physical inspection can effectively identify or deter potential threats, and thus reduce the risk of being attacked. However, doing so on every passenger may be very costly and create delays and complaints, decreasing the welfare of the approver (airports and airlines) [2]. A tradeoff is to use passenger profiling to identify potential attackers who constitute only a very small fraction of passengers: if profiling can correctly identify likely attackers and likely normal passengers, in-depth screening and inspection efforts can be better tailored to target the likely attackers. Such a mechanism has been used in aviation security management for a long time in order to reduce the inconvenience caused to normal passengers and the overall cost of security in transportation systems [3].