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A deep learning-based method for intrusion detection in smart grid: a case study of distributed denial of service detection

A deep learning-based method for intrusion detection in smart grid: a case study of distributed denial of service detection
A deep learning-based method for intrusion detection in smart grid: a case study of distributed denial of service detection
Bidirectional long short-term memory, Convolutional neural network, Deep learning, Distributed denial of service, Smart grid
IEEE
Dehghan, Fariba
e0863ef3-0a6c-467f-87ed-824cbd16408c
Dehghan, Fariba
e0863ef3-0a6c-467f-87ed-824cbd16408c

Dehghan, Fariba (2024) A deep learning-based method for intrusion detection in smart grid: a case study of distributed denial of service detection. In 2024 28th International Electrical Power Distribution Conference (EPDC). IEEE.. (doi:10.1109/EPDC62178.2024.10571748).

Record type: Conference or Workshop Item (Paper)

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More information

Published date: 28 June 2024
Keywords: Bidirectional long short-term memory, Convolutional neural network, Deep learning, Distributed denial of service, Smart grid

Identifiers

Local EPrints ID: 505444
URI: http://eprints.soton.ac.uk/id/eprint/505444
PURE UUID: f5f1d95d-a1d1-4793-b021-d593c05745ae
ORCID for Fariba Dehghan: ORCID iD orcid.org/0009-0002-0319-7905

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Date deposited: 08 Oct 2025 16:55
Last modified: 09 Oct 2025 02:22

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Author: Fariba Dehghan ORCID iD

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