2023
DOI: 10.1109/mis.2023.3252810
|View full text |Cite
|
Sign up to set email alerts
|

Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2023
2023
2025
2025

Publication Types

Select...
4
2

Relationship

0
6

Authors

Journals

citations
Cited by 9 publications
(2 citation statements)
references
References 11 publications
0
2
0
Order By: Relevance
“…In the case of unmanned aerial systems, for example, it is crucial to model the co-evolution among multiple features in time series. To tackle this challenge, Ma et al 6 proposed a novel approach that integrates Bayesian networks and normalizing flows. They developed graphical normalizing flows (GNF), a graphbased autoregressive deep learning model that performs anomaly detection in multivariate time series through density estimation.…”
Section: Time Series Anomaly Detectionmentioning
confidence: 99%
“…In the case of unmanned aerial systems, for example, it is crucial to model the co-evolution among multiple features in time series. To tackle this challenge, Ma et al 6 proposed a novel approach that integrates Bayesian networks and normalizing flows. They developed graphical normalizing flows (GNF), a graphbased autoregressive deep learning model that performs anomaly detection in multivariate time series through density estimation.…”
Section: Time Series Anomaly Detectionmentioning
confidence: 99%
“…Security/Privacy Vehicle Safety and Fault Detection (GNF) models can be utilized for anomaly detection in UV swarms [120]. GNF leverages Bayesian networks to identify relationships among time series components and perform density estimation in the low-density regions of a distribution where anomalies typically occur.…”
Section: B Typical Gai Modelsmentioning
confidence: 99%