Big-Data Driven Anomaly Detection In Vehicular Social Networks Using Graph Autoencoders

Austin Hamilton; Mohammad S. Khan; Simone Silvestri; Chandler Scott

doi:10.1109/WPMC63271.2024.10863691

Big-Data Driven Anomaly Detection In Vehicular Social Networks Using Graph Autoencoders

Austin Hamilton
, Mohammad S. Khan
, Simone Silvestri
, Chandler Scott

East Tennessee State University
University of Kentucky

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350392319
DOIs	https://doi.org/10.1109/WPMC63271.2024.10863691
State	Published - 2024
Event	27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024 - Greater Noida, India Duration: Nov 17 2024 → Nov 20 2024

Publication series

Name	International Symposium on Wireless Personal Multimedia Communications, WPMC
ISSN (Print)	1347-6890

Conference

Conference	27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024
Country/Territory	India
City	Greater Noida
Period	11/17/24 → 11/20/24

ASJC Scopus Subject Areas

Computer Networks and Communications
Computer Science Applications
Human-Computer Interaction

Keywords

data
deep learning
graph
network
vehicle

Access to Document

10.1109/WPMC63271.2024.10863691

Cite this

Hamilton, A., Khan, M. S., Silvestri, S., & Scott, C. (2024). Big-Data Driven Anomaly Detection In Vehicular Social Networks Using Graph Autoencoders. In 27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024 (International Symposium on Wireless Personal Multimedia Communications, WPMC). IEEE Computer Society. https://doi.org/10.1109/WPMC63271.2024.10863691

Big-Data Driven Anomaly Detection In Vehicular Social Networks Using Graph Autoencoders. / Hamilton, Austin; Khan, Mohammad S.; Silvestri, Simone et al.
27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024. IEEE Computer Society, 2024. (International Symposium on Wireless Personal Multimedia Communications, WPMC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hamilton, A, Khan, MS, Silvestri, S & Scott, C 2024, Big-Data Driven Anomaly Detection In Vehicular Social Networks Using Graph Autoencoders. in 27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024. International Symposium on Wireless Personal Multimedia Communications, WPMC, IEEE Computer Society, 27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024, Greater Noida, India, 11/17/24. https://doi.org/10.1109/WPMC63271.2024.10863691

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title = "Big-Data Driven Anomaly Detection In Vehicular Social Networks Using Graph Autoencoders",

keywords = "data, deep learning, graph, network, vehicle",

author = "Austin Hamilton and Khan, \{Mohammad S.\} and Simone Silvestri and Chandler Scott",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024 ; Conference date: 17-11-2024 Through 20-11-2024",

year = "2024",

doi = "10.1109/WPMC63271.2024.10863691",

language = "English",

series = "International Symposium on Wireless Personal Multimedia Communications, WPMC",

publisher = "IEEE Computer Society",

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AU - Hamilton, Austin

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PY - 2024

Y1 - 2024

KW - data

KW - deep learning

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KW - vehicle

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DO - 10.1109/WPMC63271.2024.10863691

M3 - Conference contribution

AN - SCOPUS:85217860716

T3 - International Symposium on Wireless Personal Multimedia Communications, WPMC

BT - 27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

PB - IEEE Computer Society

T2 - 27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

Y2 - 17 November 2024 through 20 November 2024

ER -

Big-Data Driven Anomaly Detection In Vehicular Social Networks Using Graph Autoencoders

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this