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Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_092f990f30a44ee8a8cf4249612f8520

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

About this item

Full title

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

Publisher

Basel: MDPI AG

Journal title

Applied sciences, 2021-09, Vol.11 (18), p.8383

Language

English

Formats

Publication information

Publisher

Basel: MDPI AG

More information

Scope and Contents

Contents

The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at ris...

Alternative Titles

Full title

Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_092f990f30a44ee8a8cf4249612f8520

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_092f990f30a44ee8a8cf4249612f8520

Other Identifiers

ISSN

2076-3417

E-ISSN

2076-3417

DOI

10.3390/app11188383

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