Deep Learning-Based Intrusion Detection Systems: A Systematic Review
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04274644%3A_____%2F21%3A%230000785" target="_blank" >RIV/04274644:_____/21:#0000785 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9483916" target="_blank" >https://ieeexplore.ieee.org/document/9483916</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2021.3097247" target="_blank" >10.1109/ACCESS.2021.3097247</a>
Alternative languages
Result language
angličtina
Original language name
Deep Learning-Based Intrusion Detection Systems: A Systematic Review
Original language description
Nowadays, the ever-increasing complication and severity of security attacks on computer networks have inspired security researchers to incorporate different machine learning methods to protect the organizations’ data and reputation. Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts. This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. It first presents the primary background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods utilized in each of them. It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
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Volume of the periodical
9
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
26
Pages from-to
101574-101599
UT code for WoS article
000679942900001
EID of the result in the Scopus database
2-s2.0-85110876289