LRDADF: An AI Enabled Framework for Detecting Low-Rate DDoS Attacks in Cloud Computing Environments-Reference-Cited by-同舟云学术

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LRDADF: An AI Enabled Framework for Detecting Low-Rate DDoS Attacks in Cloud Computing Environments

Published:2023-02-22 Issue: Volume: Page:
ISSN:
Container-title:2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)
language:
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Author:

Venkateshwarlu V.¹,Ranjith Durgunala²,Raju Amireddy¹

Affiliation:

1. Balaji Institute of Technology and Science,Department of Computer Science and Engineering,Warangal,Telangana,India

2. School of Computer Science & Artificial Intelligence, SR University,Warangal,Telangana,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10179605/10179606/10179834.pdf?arnumber=10179834

Reference50 articles.

1. An early detection of low rate DDoS attack to SDN based data center networks using information distance metrics

2. Low-Rate DoS Attack Detection Using PSD Based Entropy and Machine Learning

3. Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment;bhushan;Journal of Ambient Intelligence and Humanized Computing,2018

4. FlowTrApp: An SDN based architecture for DDoS attack detection and mitigation in data centers

5. CPSS LR-DDoS Detection and Defense in Edge Computing Utilizing DCNN Q-Learning

Cited by 1 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. A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection;Scientific Reports;2024-03-01

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