Automatic dependent surveillance-broadcast (ADS-B) anomalous messages and attack type detection: deep learning-based architecture.

Abstract

Automatic Dependent Surveillance-Broadcast (ADS-B) is a vital communication protocol within air traffic control (ATC) systems. Unlike traditional technologies, ADS-B utilizes the Global Positioning System (GPS) to deliver more accurate and precise location data while reducing operational and deployment costs. It enhances radar coverage and serves as a standalone solution in areas lacking radar services. Despite these advantages, ADS-B faces significant security vulnerabilities due to its open design and the absence of built-in security features. Given its critical role, developing an advanced security framework to classify ADS-B messages and identify various attack types is essential to safeguard the system. This research makes several key contributions to address these challenges. First, it presents a comprehensive review of state-of-the-art machine learning and deep learning techniques, critically analyzing existing methodologies for ADS-B intrusion detection. Second, a detailed attack model is developed, categorizing potential threats and aligning them with key security requirements, including confidentiality, integrity, availability, and authentication. Third, the study proposes a robust and accurate Intrusion Detection System (IDS) using three advanced deep learning models-TabNet, Neural Oblivious Decision Ensembles (NODE), and DeepGBM-to classify ADS-B messages and detect specific attack types. The models are evaluated using standard metrics, including accuracy, precision, recall, and F1-score. Among the tested models, DeepGBM achieves the highest accuracy at 98%, outperforming TabNet (92%) and NODE (96%). The findings offer valuable insights into ADS-B security and define essential requirements for a future security framework, contributing actionable recommendations for mitigating threats in this critical communication protocol.