• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of business data communications and networking >A Survey on Intrusion Detection System for Software Defined Networks (SDN)
【24h】

A Survey on Intrusion Detection System for Software Defined Networks (SDN)

机译：软件定义网络（SDN）入侵检测系统研究

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.
机译：当前，互联网向广泛增长的发展以及传统网络的静态性质限制了其满足组织业务需求的能力。新的网络架构软件定义的网络（SDN）似乎可以解决这些挑战并提供独特的功能。但是，这些SDN的可编程和集中式方法面临着新的安全挑战，需要创新的安全机制，例如入侵检测系统（IDS）。 SDN的IDS目前采用机器学习方法进行设计。但是，还正在探索深度学习方法，以实现更好的效率和准确性。在本文中，将解释SDN的概述以及它的安全性和IDS作为安全解决方案。本文讨论了旨在保护SDN的现有安全解决方案的调查，以及基于深度学习模型和机器学习方法的各种IDS方法的比较研究。最后，我们描述了SDN安全的未来方向。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号