Machine Learning and Deep Learning Approaches for CyberSecurity: A Review-Reference-Cited by-同舟云学术

Machine Learning and Deep Learning Approaches for CyberSecurity: A Review

Published:2022 Issue: Volume:10 Page:19572-19585
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Halbouni Asmaa¹,Gunawan Teddy Surya¹^ORCID,Habaebi Mohamed Hadi¹^ORCID,Halbouni Murad²,Kartiwi Mira³^ORCID,Ahmad Robiah⁴^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia

2. Department of Natural, Engineering and Technology Sciences, Arab American University, Jenin, Palestine

3. Information Systems Department, International Islamic University Malaysia, Kuala Lumpur, Malaysia

4. Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

Funder

Universiti Teknologi Malaysia through Collaborative Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09712274.pdf?arnumber=9712274

Reference47 articles.

1. Deep learning approach for intrusion detection system (IDS) in the Internet of Things (IoT) network using gated recurrent neural networks (GRU);putchala,2017

2. Tiki-Taka

3. An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks

4. Enhanced Network Anomaly Detection Based on Deep Neural Networks

5. A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

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