Analyzing Sequences of Airspace States to Detect Anomalous Traffic Conditions

Habler, Edan; Shabtai, Asaf

doi:10.1109/taes.2021.3124199

Cited by 9 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, deep-learning-based ADS-B anomaly detection models could also be attacked by adversarial examples. The applications of ADS-B anomaly detection models end with decision making by pilots or onboard automation systems [ 16 ]. Therefore, it may result in serious consequences such as flight deviation, flight delays and aircraft collisions once deep-learning-based ADS-B anomaly detection models are attacked by adversarial examples [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Attacks against Deep-Learning-Based Automatic Dependent Surveillance-Broadcast Unsupervised Anomaly Detection Models in the Context of Air Traffic Management

Luo,

Wang,

Tian

et al. 2024

Sensors

View full text Add to dashboard Cite

Deep learning has shown significant advantages in Automatic Dependent Surveillance-Broadcast (ADS-B) anomaly detection, but it is known for its susceptibility to adversarial examples which make anomaly detection models non-robust. In this study, we propose Time Neighborhood Accumulation Iteration Fast Gradient Sign Method (TNAI-FGSM) adversarial attacks which fully take into account the temporal correlation of an ADS-B time series, stabilize the update directions of adversarial samples, and escape from poor local optimum during the process of iterating. The experimental results show that TNAI-FGSM adversarial attacks can successfully attack ADS-B anomaly detection models and improve the transferability of ADS-B adversarial examples. Moreover, the TNAI-FGSM is superior to two well-known adversarial attacks called the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). To the best of our understanding, we demonstrate, for the first time, the vulnerability of deep-learning-based ADS-B time series unsupervised anomaly detection models to adversarial examples, which is a crucial step in safety-critical and cost-critical Air Traffic Management (ATM).

show abstract

Section: Introductionmentioning

confidence: 99%