IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with燚eep燣earning-Reference-Cited by-同舟云学术

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IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with燚eep燣earning

Published:2023 Issue:2 Volume:74 Page:3085-3100
ISSN:1546-2226
Container-title:Computers, Materials & Continua
language:en
Short-container-title:

Author:

Masoud Alshahrani Saeed,S. Alrayes Fatma,Alqahtani Hamed,S. Alzahrani Jaber,Maray Mohammed,Alazwari Sana,A. Shamseldin Mohamed,Al Duhayyim Mesfer

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Mechanics of Materials,Modeling and Simulation,Biomaterials

Link

https://www.techscience.com/cmc/v74n2/50266/pdf

Reference26 articles.

1. Overview of botnet detection based on machine learning;Dong,2018

2. Cyber security in IoT-based cloud computing: A comprehensive survey;Ahmad;Electronics,2021

3. Artificial intelligence based network intrusion detection with hyper-parameter optimization tuning on the realistic cyber dataset cse-cic-ids2018 using cloud computing;Kanimozhi;ICT Express,2019

4. Analysis of Cybersecurity Threats in Cloud Applications Using Deep Learning Techniques

5. A review on honeypot-based botnet detection models for smart factory;Seungjin;International Journal of Advanced Computer Science and Applications,2020

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning;IEEE Access;2024

2. Enhancing cybersecurity in cloud-assisted Internet of Things environments: A unified approach using evolutionary algorithms and ensemble learning;AIMS Mathematics;2024

3. Comprehensive Survey on AI-Based Technologies for Enhancing IoT Privacy and Security: Trends, Challenges, and Solutions;HUM-CENT COMPUT INFO;2023

4. Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment;IEEE Access;2023

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