A Comprehensive Method for Anomaly Detection in Complex Dynamic IoT Systems
Modern dynamic systems, such as transportation networks and IoT infrastructures, generate massive volumes of interrelated temporal data represented as temporal graphs. Conventional methods – like clustering, statistical thresholds, and classical time series analysis – often fail to account for the s...
Saved in:
| Main Authors: | Andrii Liashenko, Larysa Globa |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Anhalt University of Applied Sciences
2025-04-01
|
| Series: | Proceedings of the International Conference on Applied Innovations in IT |
| Subjects: | |
| Online Access: | https://icaiit.org/paper.php?paper=13th_ICAIIT_1/2_4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
by: Chi Zhang, et al.
Published: (2025-08-01) -
A Temporal Graph Network Algorithm for Detecting Fraudulent Transactions on Online Payment Platforms
by: Diego Saldaña-Ulloa, et al.
Published: (2024-12-01) -
Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs
by: Samir Abdaljalil, et al.
Published: (2025-01-01) -
Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions
by: Shiyang Chen, et al.
Published: (2025-08-01) -
ViGLAD: Vision Graph Neural Networks for Logical Anomaly Detection
by: Firas Zoghlami, et al.
Published: (2024-01-01)