Optimized MLP-CNN model to enhance detecting DDoS attacks in SDN environment
In the contemporary landscape, Distributed Denial of Service (DDoS) attacks have emerged
as an exceedingly pernicious threat, particularly in the context of network management …
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
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 …
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.
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 …
(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
As cyber threat actors develop increasingly sophisticated strategies, cutting-edge cyber
security is necessary for industry organizations and government agencies. A security threat …
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
Software Defined Networks (SDN) have revolutionized multimedia communication systems
with their dynamic resource allocation and load balancing capabilities. However, ensuring …
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 …
been given different possibilities for even more advanced assaults on specific targets in …
DDoS detection using hybrid deep neural network approaches
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 …
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
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 …
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 …
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 …
balance the supply and demand of electricity. As an important interactive resource of the …