Convolutional neural network approach for anomaly-based intrusion detection on IoT-enabled smart space orchestration system

Upman, Vikas; Goranin, Nikolaj; Čenys, Antanas

doi:10.15388/DAMSS.13.2022

Use this URL to cite this publication: https://hdl.handle.net/20.500.14911/153247

Convolutional neural network approach for anomaly-based intrusion detection on IoT-enabled smart space orchestration system

Details

Publication Type (CRIS)

Tezės kitoje duomenų bazėje / Theses in other database (T1c)

Publication Type (eLABa)

Konferencijos pranešimo tezės tarptautinėse DB / Conference presentation abstract in an international DB (T1)

VILNIUS TECH Research Priorities and Topics

Informacijos ir informacinių technologijų sauga / Information and Information Technologies Security (IK0101)

Lithuanian Intelligent Specialization

Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies (L106)

Author(s)

Author	Affiliation
Upman, Vikas	Informacinių sistemų katedra / Department of Information Systems (1046)
Goranin, Nikolaj	Informacinių sistemų katedra / Department of Information Systems (1046)
Čenys, Antanas	Informacinių sistemų katedra / Department of Information Systems (1046)

Title

Convolutional neural network approach for anomaly-based intrusion detection on IoT-enabled smart space orchestration system

[en]

Vikas Upman, Nikolaj Goranin, Antanas Čenys

Is part of

DAMSS 2022: 13th conference on data analysis methods for software systems, Druskininkai, Lithuania, December 1–3, 2022

Published In

Year
2022

Publisher

Vilnius : Vilniaus universitetas, 2022

Referenced in Database(s)(DBs)

Dimensions

Extent

p. 95

URI

URI	Access Rights
https://hdl.handle.net/20.500.14911/153247
leidinys internete	Viso teksto dokumentas (atviroji prieiga) / Full Text Document (Open Access)
https://www.zurnalai.vu.lt/proceedings/issue/archive	Leidėjo puslapis / Publisher Website
https://talpykla.elaba.lt/elaba-fedora/objects/elaba:148980058/datastreams/MAIN/content

DOI

10.15388/DAMSS.13.2022

Science / Art Area

Technologijos mokslai / Technological sciences (T)

Field of Science / Art

Informatikos inžinerija / Informatics engineering (T007)

Resource Type (COAR)

TextConference outputConference proceedingsConference paper

Language

Anglų / English (en)

Country

Lietuva / Lithuania (LT)

Description

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.