当前位置: X-MOL 学术Comput. Sci. Rev. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Detection and mitigation of DDoS attacks in SDN: A comprehensive review, research challenges and future directions
Computer Science Review ( IF 13.3 ) Pub Date : 2020-06-20 , DOI: 10.1016/j.cosrev.2020.100279
Jagdeep Singh , Sunny Behal

Many security solutions have been proposed in the past to protect Internet architecture from a diversity of malware. However, the security of the Internet and its applications is still an open research challenge. Researchers continuously working on novel network architectures such as HTTP as the narrow waist, Named Data Networking (NDN), programmable networks and Software-Defined Networking (SDN) for designing a more reliable network. Among these, SDN has emerged as a more robust and secure solution to combat against such malicious activities. In SDN, bifurcation of control plane and data plane provides more manageability, control, dynamic updating of rules, analysis, and global view of the network using a centralized controller. Though SDN seems a secured network architecture as compared to the conventional IP-based networks, still, SDN itself is vulnerable to many types of network intrusions and facing severe deployment challenges. This paper systematically reviews around 70 prominent DDoS detection and mitigation mechanisms in SDN networks. These mechanisms are characterized into four categories, viz: Information theory-based methods, Machine learning-based methods, Artificial Neural Networks (ANN) based methods and other miscellaneous methods. The paper also dowries and deliberates on various open research issues, gaps and challenges in the deployment of a secure SDN-based DDoS defence solution. Such an exhaustive review will surely help the researcher community to provide more robust and reliable DDoS solutions in SDN networks.



中文翻译:

SDN中DDoS攻击的检测和缓解:全面回顾,研究挑战和未来方向

过去已经提出了许多安全解决方案,以保护Internet体系结构免受各种恶意软件的侵害。但是,Internet及其应用程序的安全性仍然是一个开放的研究挑战。研究人员不断研究新颖的网络体系结构,例如HTTP(如窄腰),命名数据网络(NDN),可编程网络和软件定义网络(SDN),以设计更可靠的网络。其中,SDN已经成为一种更强大,更安全的解决方案,可以抵御此类恶意活动。在SDN中,控制平面和数据平面的分叉提供了更高的可管理性,控制,规则的动态更新,分析以及使用集中控制器的网络全局视图。尽管与传统的基于IP的网络相比,SDN似乎是一种安全的网络架构,SDN本身很容易遭受多种类型的网络入侵,并面临严峻的部署挑战。本文系统地回顾了SDN网络中约70种著名的DDoS检测和缓解机制。这些机制分为四个类别,分别是:基于信息论的方法,基于机器学习的方法,基于人工神经网络(ANN)的方法和其他各种方法。本文还对部署基于SDN的安全DDoS防御解决方案中的各种开放性研究问题,差距和挑战进行了探讨。这样详尽的审查必将帮助研究人员社区在SDN网络中提供更强大和可靠的DDoS解决方案。本文系统地回顾了SDN网络中约70种著名的DDoS检测和缓解机制。这些机制分为四类,即基于信息论的方法,基于机器学习的方法,基于人工神经网络(ANN)的方法和其他各种方法。本文还对部署基于SDN的安全DDoS防御解决方案中的各种开放性研究问题,差距和挑战进行了探讨。这样详尽的审查必将帮助研究人员社区在SDN网络中提供更强大和可靠的DDoS解决方案。本文系统地回顾了SDN网络中约70种著名的DDoS检测和缓解机制。这些机制分为四类,即基于信息论的方法,基于机器学习的方法,基于人工神经网络(ANN)的方法和其他各种方法。本文还对基于SDN的安全DDoS防御解决方案的部署中的各种开放性研究问题,差距和挑战进行了探讨。这样详尽的审查必将帮助研究人员社区在SDN网络中提供更强大和可靠的DDoS解决方案。基于人工神经网络(ANN)的方法和其他各种方法。本文还对部署基于SDN的安全DDoS防御解决方案中的各种开放性研究问题,差距和挑战进行了探讨。这样详尽的审查必将帮助研究人员社区在SDN网络中提供更强大和可靠的DDoS解决方案。基于人工神经网络(ANN)的方法和其他各种方法。本文还对部署基于SDN的安全DDoS防御解决方案中的各种开放性研究问题,差距和挑战进行了探讨。这样详尽的审查必将帮助研究人员社区在SDN网络中提供更强大和可靠的DDoS解决方案。

更新日期:2020-06-20
down
wechat
bug