Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework-Reference-Cited by-同舟云学术

Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework

Published:2022 Issue: Volume:10 Page:53015-53026
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Razib Mohammad Al¹,Javeed Danish²^ORCID,Khan Muhammad Taimoor³^ORCID,Alkanhel Reem⁴^ORCID,Muthanna Mohammed Saleh Ali⁵^ORCID

Affiliation:

1. School of Computer Science, Changchun University of Science and Technology, Changchun, China

2. Software College, Northeastern University, Shenyang, China

3. Riphah Institute of Science and Engineering, Islamabad, Pakistan

4. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

5. Institute of Computer Technologies and Information Security, Southern Federal University, Taganrog, Russia

Funder

Princess Nourah bint Abdulrahman University Researchers Supporting Project

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

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/ielx7/6287639/9668973/09766348.pdf?arnumber=9766348

Reference64 articles.

1. RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks

2. Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

3. Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

4. Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods

5. An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks

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