An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder: A Review

Muaadh A. Alsoufi; Maheyzah Md Siraj; Fuad A. Ghaleb; Aya Hasan Abdulqader; Elham Ali; Maryam Omar

doi:10.1007/978-3-031-59707-7_20

An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder: A Review

Muaadh A. Alsoufi, Maheyzah Md Siraj, Fuad A. Ghaleb, Aya Hasan Abdulqader, Elham Ali, Maryam Omar

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Citation (SciVal)

Original language	English
Title of host publication	Lecture Notes on Data Engineering and Communications Technologies
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	224-239
Number of pages	16
DOIs	https://doi.org/10.1007/978-3-031-59707-7_20
Publication status	Published (VoR) - 2024

Publication series

Name	Lecture Notes on Data Engineering and Communications Technologies
Volume	211
ISSN (Print)	2367-4512
ISSN (Electronic)	2367-4520

Keywords

Anomaly Intrusion Detection
Autoencoder
Cybersecurity
Deep Learning
IoT Applications
Network Security

Access to Document

10.1007/978-3-031-59707-7_20

Cite this

Alsoufi, M. A., Siraj, M. M., Ghaleb, F. A., Abdulqader, A. H., Ali, E., & Omar, M. (2024). An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder: A Review. In Lecture Notes on Data Engineering and Communications Technologies (pp. 224-239). (Lecture Notes on Data Engineering and Communications Technologies; Vol. 211). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-59707-7_20

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title = "An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder: A Review",

keywords = "Anomaly Intrusion Detection, Autoencoder, Cybersecurity, Deep Learning, IoT Applications, Network Security",

author = "Alsoufi, \{Muaadh A.\} and Siraj, \{Maheyzah Md\} and Ghaleb, \{Fuad A.\} and Abdulqader, \{Aya Hasan\} and Elham Ali and Maryam Omar",

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language = "English",

series = "Lecture Notes on Data Engineering and Communications Technologies",

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Alsoufi, MA, Siraj, MM, Ghaleb, FA, Abdulqader, AH, Ali, E & Omar, M 2024, An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder: A Review. in Lecture Notes on Data Engineering and Communications Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol. 211, Springer Science and Business Media Deutschland GmbH, pp. 224-239. https://doi.org/10.1007/978-3-031-59707-7_20

An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder: A Review. / Alsoufi, Muaadh A.; Siraj, Maheyzah Md; Ghaleb, Fuad A. et al.
Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH, 2024. p. 224-239 (Lecture Notes on Data Engineering and Communications Technologies; Vol. 211).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder

T2 - A Review

AU - Alsoufi, Muaadh A.

AU - Siraj, Maheyzah Md

AU - Ghaleb, Fuad A.

AU - Abdulqader, Aya Hasan

AU - Ali, Elham

AU - Omar, Maryam

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

KW - Anomaly Intrusion Detection

KW - Autoencoder

KW - Cybersecurity

KW - Deep Learning

KW - IoT Applications

KW - Network Security

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DO - 10.1007/978-3-031-59707-7_20

M3 - Chapter

AN - SCOPUS:85194517616

T3 - Lecture Notes on Data Engineering and Communications Technologies

SP - 224

EP - 239

BT - Lecture Notes on Data Engineering and Communications Technologies

PB - Springer Science and Business Media Deutschland GmbH

ER -

Alsoufi MA, Siraj MM, Ghaleb FA, Abdulqader AH, Ali E, Omar M. An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder: A Review. In Lecture Notes on Data Engineering and Communications Technologies. Springer Science and Business Media Deutschland GmbH. 2024. p. 224-239. (Lecture Notes on Data Engineering and Communications Technologies). doi: 10.1007/978-3-031-59707-7_20

An Anomaly Intrusion Detection Systems in IoT Based on Autoencoder: A Review

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