Journal Article10.1007/s10586-024-04989-0
Advanced SDN-based network security: an ensemble optimized deep learning-based framework for mitigating DDoS attacks with intrusion detection
Dandugudum Mahesh,Sampath Kumar Tallapally +1 more
About: This article is published in Cluster Computing. The article was published on 28 Apr 2025.
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References
•Book
Bioseparations Science and Engineering
Roger G. Harrison
- 01 Jan 2002
TL;DR: 1. INTRODUCTION to BIOPRODUCTs and BIOSEPARATIONS 2. ANALYTICAL METHODs 3. CELL LYSIS and FLOCCULATION 4. FILTRATION 5. SEDIMENTATION 6. EXTRACTION 7. LIQUID CHROMATOGRAPHY and ADSORPTION 8. PRECIPITATION 9. CRYSTALLIZATION 10. DRYING 11. BIOPrOCESS
466
Securing IoT and SDN systems using deep-learning based automatic intrusion detection
TL;DR: In this paper , the authors proposed a Secured Automatic Two-Level Intrusion Detection System (SATIDS) based on an improved Long Short-Term Memory (LSTM) network.
64
FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks
TL;DR: In this paper , the authors proposed an SDN-based, four-module DDoS attack detection and mitigation framework for IoT networks called FMDADM, which comprises four main modules and five-tier architecture.
35
DDoS attack detection and mitigation using deep neural network in SDN environment
Vanlalruata Hnamte,Ashfaq Ahmad Najar,Hong Nhung-Nguyen,Jamal Hussain,Manohar Naik S +4 more
- 01 Dec 2023
TL;DR: This research proposes a deep neural network (DNN) architecture for DDoS attack detection in SDN environments, achieving 99.98-100% detection accuracy and low loss rates, outperforming traditional techniques and offering insights for network security professionals.
33
Cyber-Secure SDN: A CNN-Based Approach for Efficient Detection and Mitigation of DDoS Attacks
Ashfaq Ahmad Najar,Manohar Naik S +1 more
- 01 Jan 2024
TL;DR: This paper proposes a CNN-based approach using Balanced Random Sampling (BRS) for efficient DDoS attack detection in SDN environments, achieving 99.99% accuracy in binary classification and 98.64% in multi-classification, and demonstrates its superiority over existing literature.
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