安全软件定义网络的机器学习方法:机器学习和人工智能

Afaf D. Althobiti, Rabab M. Almohayawi, O. Bamasag
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引用次数: 1

摘要

本文提出了一种网络安全增强方案,旨在提高软件定义网络(SDN)网络攻击检测的性能水平,防止拒绝服务攻击。我们将采用两种解决方案,并对SDN攻击检测性能进行比较。第一种方法是SDN与IDS过程的性能准确性,第二种方法是SDN与机器学习的集成。该项目一般服务于信息安全、网络安全和网络安全意识领域的组织。系统性能评估结果表明,该系统能够提供有效的DDoS攻击检测,并为软件定义网络提供安全性增强。
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Machine Learning approach to Secure Software Defined Network: Machine Learning and Artificial Intelligence
This paper proposes network security enhancement solution aiming to improving the level of performance in the detection of cyber-attacks on Software Defined Network (SDN) it will prevent against Denial of Service Attack. We are going to employ two solution and comparing on the SDN attack detection performance. The first approach is the performance accuracy of the SDN with IDS procedural, and the second approach is the integration of SDN with Machine Learning. The project serves the organization generally in the field of information security, network security and cybersecurity awareness. The system performance evaluation results prove the system is capable to provide the effective DDoS attack detection and provide security enhancement in Software Defined Network.
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