ADS-B Spoofing Interference Detection Using Improved AlexNet

Automatic Dependent Surveillance-Broadcast (ADS-B) technology is a new air traffic surveillance technology that follows the guiding principles of “air-ground integration” and “global interoperability” to achieve track information sharing. However, its open architecture makes it extremely vulnerable to a variety of spoofing attacks, which seriously threatens air traffic safety. In this paper, aiming at the characteristics of Doppler frequency offset variation of ADS-B message and the change rule of reporting position, combined with the machine learning method represented by deep learning, this paper proposes to use the improved AlexNet extraction feature and detect fraud interference. Compared with the traditional signal processing method, the method reduces the complexity of the algorithm and improves the recognition accuracy, especially when the track length is short. Simulation experiments verify the effectiveness of the method.