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Detecting ADS-B Spoofing Attacks using Deep Neural Networks by Ying, Xuhang; Mazer, Joanna; Bernieri, Giuseppe; Conti, Mauro; Bushnell, Linda; Poovendran, Radha is a scholarly article available to read on EtoBox.

What is Detecting ADS-B Spoofing Attacks using Deep Neural Networks about?

The Automatic Dependent Surveillance-Broadcast (ADS-B) system is a key component of the Next Generation Air Transportation System (NextGen) that manages the increasingly congested airspace. It provides accurate aircraft localization and efficient air traffic management and also improves the safety of billions of current and future passengers. While the benefits of ADS-B are well known, the lack of basic security measures like encryption and authentication introduces various exploitable security vulnerabilities. One practical threat is the ADS-B spoofing attack that targets the ADS-B ground station, in which the ground-based or aircraft-based attacker manipulates the International Civil Aviation Organization (ICAO) address (a unique identifier for each aircraft) in the ADS-B messages to fake the appearance of non-existent aircraft or masquerade as a trusted aircraft. As a result, this attack can confuse the pilots or the air traffic control personnel and cause dangerous maneuvers. In this paper, we introduce SODA - a two-stage Deep Neural Network (DNN)-based spoofing detector for ADS-B that consists of a message classifier and an aircraft classifier. It allows a ground station to ex

Author

Ying, Xuhang; Mazer, Joanna; Bernieri, Giuseppe; Conti, Mauro; Bushnell, Linda; Poovendran, Radha

Published

2019

Language

EN