학술논문

A Compressive Study on Detection Accuracy Model for DoS Attack in SDN Using Ensemble Learning Techniques
Document Type
Conference
Source
2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS) Innovative Approaches in Smart Technologies (ISAS), 2023 7th International Symposium on. :1-6 Nov, 2023
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Signal Processing and Analysis
Regulators
Denial-of-service attack
Regulation
Ensemble learning
Software defined networking
Computer crime
Monitoring
DDoS
SDN
Machine Learning Techniques
SVM
Neural
Logistic Regression
Language
Abstract
The possible networking architecture known as a “Software-defined Network” (SDN) separates the information and management layers and offers polarized control over the network. This new approach considers responsibilities and empowers network administrators to electronically assign, manage, adjust, and monitor clan behaviour. One important advantage of SDN is its polarising power, which may occasionally cause a serious breach. The snitcher will have access to the complete framework if he is successful in getting to the controller’s core. The regulators are utterly powerless to combat Distributed Denial of Service (DDoS) attacks, which wear down the model and make the administrators of the regulations inaccessible. It’s crucial to identify potential dangers in the controllers early on. As a result, many algorithms and processes.