A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system

Publication typeJournal Article
Publication date2022-01-07
scimago Q1
wos Q2
SJR0.888
CiteScore12.1
Impact factor3.9
ISSN17517575, 17517583
Computer Science Applications
Information Systems and Management
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GOST |
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GOST Copy
Gaurav A. et al. A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system // Enterprise Information Systems. 2022. pp. 1-25.
GOST all authors (up to 50) Copy
Gaurav A., Gupta B. D., Panigrahi P. K. A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system // Enterprise Information Systems. 2022. pp. 1-25.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1080/17517575.2021.2023764
UR - https://doi.org/10.1080/17517575.2021.2023764
TI - A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system
T2 - Enterprise Information Systems
AU - Gaurav, Akshat
AU - Gupta, B. Das
AU - Panigrahi, Prabin Kumar
PY - 2022
DA - 2022/01/07
PB - Taylor & Francis
SP - 1-25
SN - 1751-7575
SN - 1751-7583
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2022_Gaurav,
author = {Akshat Gaurav and B. Das Gupta and Prabin Kumar Panigrahi},
title = {A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system},
journal = {Enterprise Information Systems},
year = {2022},
publisher = {Taylor & Francis},
month = {jan},
url = {https://doi.org/10.1080/17517575.2021.2023764},
pages = {1--25},
doi = {10.1080/17517575.2021.2023764}
}