Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey-Reference-Cited by-同舟云学术

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

Published:2022 Issue: Volume:10 Page:121173-121192
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Jayalaxmi P. L. S.¹^ORCID,Saha Rahul¹^ORCID,Kumar Gulshan¹^ORCID,Conti Mauro²^ORCID,Kim Tai-Hoon³^ORCID

Affiliation:

1. School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

2. Department of Mathematics, University of Padua, Padua, Italy

3. Glocal Campus, Konkuk University, Chungju-si, South Korea

Funder

European Union’s Horizon 2020 Research and Innovation Program for the Project COmprehensive cyber-intelligence framework for resilient coLLABorative manufacturing Systems

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09941074.pdf?arnumber=9941074

Reference122 articles.

1. Machine learning-driven intrusion detection for Contiki-NG-based IoT networks exposed to NSL-KDD dataset

2. Machine Learning Based Intrusion Detection Systems for IoT Applications

3. ELNIDS: Ensemble Learning based Network Intrusion Detection System for RPL based Internet of Things

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