IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction

Seo Jin Lee; Paul D. Yoo; A. Taufiq Asyhari; Yoonchan Jhi; Lounis Chermak; Chan Yeob Yeun; Kamal Taha

doi:10.1109/ACCESS.2020.2985089

IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction

Seo Jin Lee, Paul D. Yoo^*, A. Taufiq Asyhari, Yoonchan Jhi, Lounis Chermak, Chan Yeob Yeun, Kamal Taha

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

90 Citations (SciVal)

Original language	English
Article number	9055368
Pages (from-to)	65520-65529
Number of pages	10
Journal	IEEE Access
Volume	8
DOIs	https://doi.org/10.1109/ACCESS.2020.2985089
Publication status	Published (VoR) - 2020

Funding

This work was supported in part by the Samsung’s Global Outreach Fund under Grant P10458, and in part by the Center for Cyber-Physical Systems, Khalifa University, under Grant 8474000137-RC1-C2PS-T3. This work was supported in part by the Samsung's Global Outreach Fund under Grant P10458, and in part by the Center for Cyber-Physical Systems, Khalifa University, under Grant 8474000137-RC1-C2PS-T3.

Keywords

IoT security
edge computing
feature engineering
intrusion detection
machine learning
mutual information

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10.1109/ACCESS.2020.2985089

Cite this

@article{95f1cb6639f04dbdad5be624d8dc85eb,

title = "IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction",

keywords = "IoT security, edge computing, feature engineering, intrusion detection, machine learning, mutual information",

author = "Lee, \{Seo Jin\} and Yoo, \{Paul D.\} and \{Taufiq Asyhari\}, A. and Yoonchan Jhi and Lounis Chermak and Yeun, \{Chan Yeob\} and Kamal Taha",

note = "Funding Information: This work was supported in part by the Samsung{\textquoteright}s Global Outreach Fund under Grant P10458, and in part by the Center for Cyber-Physical Systems, Khalifa University, under Grant 8474000137-RC1-C2PS-T3. Funding Information: This work was supported in part by the Samsung's Global Outreach Fund under Grant P10458, and in part by the Center for Cyber-Physical Systems, Khalifa University, under Grant 8474000137-RC1-C2PS-T3. Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2020",

doi = "10.1109/ACCESS.2020.2985089",

language = "English",

volume = "8",

pages = "65520--65529",

journal = "IEEE Access",

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T2 - Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction

AU - Lee, Seo Jin

AU - Yoo, Paul D.

AU - Taufiq Asyhari, A.

AU - Jhi, Yoonchan

AU - Chermak, Lounis

AU - Yeun, Chan Yeob

AU - Taha, Kamal

N1 - Funding Information: This work was supported in part by the Samsung’s Global Outreach Fund under Grant P10458, and in part by the Center for Cyber-Physical Systems, Khalifa University, under Grant 8474000137-RC1-C2PS-T3. Funding Information: This work was supported in part by the Samsung's Global Outreach Fund under Grant P10458, and in part by the Center for Cyber-Physical Systems, Khalifa University, under Grant 8474000137-RC1-C2PS-T3. Publisher Copyright: © 2013 IEEE.

PY - 2020

Y1 - 2020

KW - IoT security

KW - edge computing

KW - feature engineering

KW - intrusion detection

KW - machine learning

KW - mutual information

UR - https://www.scopus.com/pages/publications/85084113760

UR - https://www.scopus.com/inward/citedby.url?scp=85084113760&partnerID=8YFLogxK

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DO - 10.1109/ACCESS.2020.2985089

M3 - Article

AN - SCOPUS:85084113760

SN - 2169-3536

VL - 8

SP - 65520

EP - 65529

JO - IEEE Access

JF - IEEE Access

M1 - 9055368

ER -

IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction

Funding

Keywords

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