Optimized MLP-CNN model to enhance detecting DDoS attacks in SDN environment

MA Setitra, M Fan, BLY Agbley, ZEA Bensalem - Network, 2023 - mdpi.com
In the contemporary landscape, Distributed Denial of Service (DDoS) attacks have emerged
as an exceedingly pernicious threat, particularly in the context of network management …

A novel DDoS detection and mitigation technique using hybrid machine learning model and redirect illegitimate traffic in SDN network

A Singh, H Kaur, N Kaur - Cluster Computing, 2024 - Springer
Abstract Software Defined Networking (SDN) is a paradigm shift in the network industry with
decoupling of control and data plane. This helps network engineers to control and manage …

[PDF][PDF] RMCARTAM For DDoS Attack Mitigation in SDN Using Machine Learning.

M Revathi, VV Ramalingam, B Amutha - Comput. Syst. Sci. Eng., 2023 - researchgate.net
The impact of a Distributed Denial of Service (DDoS) attack on Software Defined Networks
(SDN) is briefly analyzed. Many approaches to detecting DDoS attacks exist, varying on the …

AI-driven DDoS mitigation at the edge: Leveraging machine learning for real-time threat detection and response

S Arora, P Khare, S Gupta - 2024 International Conference on …, 2024 - ieeexplore.ieee.org
As cyber threat actors develop increasingly sophisticated strategies, cutting-edge cyber
security is necessary for industry organizations and government agencies. A security threat …

Artificial Intelligence and Quantum Synergies in Trust-Enhanced Consumer Applications for Software Defined Networks

KA Awan, IU Din, A Almogren… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Software Defined Networks (SDN) have revolutionized multimedia communication systems
with their dynamic resource allocation and load balancing capabilities. However, ensuring …

ShChain_3D-ResNet: Sharding Blockchain with 3D-Residual Network (3D-ResNet) Deep Learning Model for Classifying DDoS Attack in Software Defined Network

E Fenil, P Mohan Kumar - Symmetry, 2022 - mdpi.com
The distributed denial of service (DDoS) vulnerabilities have rapidly extended and have
been given different possibilities for even more advanced assaults on specific targets in …

DDoS detection using hybrid deep neural network approaches

V Hnamte, J Hussain - 2023 IEEE 8th International Conference …, 2023 - ieeexplore.ieee.org
In this study, we provide Deep Neural Network (DNN) based approaches to detecting
Distributed Denial-of-Service (DDoS) attacks. In order to improve the DNN's accuracy, the …

Detection of DDoS Attacks in SDN Using Machine Learning Approaches: A Review

S Chattopadhyay, AK Sahoo, S Jasola… - … Conference on Cyber …, 2023 - Springer
Over the past several years, the Distributed Denial-of-Service attack has emerged as a big
threat to Software-Defined Networks because of its frequent attack on SDNs. The DDoS …

Effective Security Mechanisms against Distributed Denial of Services

K Gaur, K Gaur, T Sachdeva, M Diwakar… - 2023 6th …, 2023 - ieeexplore.ieee.org
In general, the popularity of DDoS attacks being used as a weapon to harm the opposite
party, is on the rise. Hence, is the need for the security from such disruptive attacks. There …

Evaluation of the Machine Learning Schemes for Information Security of Demand Response

K Shi, S Chen, D Li, M Feng… - 2022 IEEE 6th Conference …, 2022 - ieeexplore.ieee.org
The demand side response has been vigorously promoted by the state in order to help
balance the supply and demand of electricity. As an important interactive resource of the …