A DDoS Vulnerability Analysis System against Distributed SDN Controllers in a Cloud Computing Environment

Author:

Badotra SumitORCID,Tanwar SarveshORCID,Bharany SalilORCID,Rehman Ateeq UrORCID,Eldin Elsayed TagORCID,Ghamry Nivin A.,Shafiq MuhammadORCID

Abstract

Software-Defined Networking (SDN) is now a well-established approach in 5G, Internet of Things (IoT) and Cloud Computing. The primary idea behind its immense popularity is the separation of its underlying intelligence from the data-carrying components like routers and switches. The intelligence of the SDN-based networks lies in the central point, popularly known as the SDN controller. It is the central control hub of the SDN-based network, which has full privileges and a global view over the entire network. Providing security to SDN controllers is one such important task. Whenever one wishes to implement SDN into their data center or network, they are required to provide the website to SDN controllers. Several attacks are becoming a hurdle in the exponential growth of SDN, and among all one such attack is a Distributed Denial of Service (DDoS) attack. In a couple of years, several new SDN controllers will be available. Among many, Open Networking Operating System (ONOS) and OpenDayLight (ODL) are two popular SDN controllers laying the foundation for many other controllers. These SDN controllers are now being used by numerous businesses, including Cisco, Juniper, IBM, Google, etc. In this paper, vulnerability analysis is carried out against DDoS attacks on the latest released versions of both ODL and ONOS SDN controllers in real-time cloud data centers. For this, we have considered distributed SDN controllers (located at different locations) on two different clouds (AWS and Azure). These controllers are connected through the Internet and work on different networks. DDoS attacks are bombarded on the distributed SDN controllers, and vulnerability is analyzed. It was observed with experimentation that, under five different scenarios (malicious traffic generated), ODL-3 node cluster controller had performed better than ONOS. In these five different scenarios, the amount of malicious traffic was incregradually increased. It also observed that, in terms of disk utilization, memory utilization, and CPU utilization, the ODL 3-node cluster was way ahead of the SDN controller.

Funder

Future University in Egypt

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Reference21 articles.

1. Fabric: A retrospective on evolving SDN;Casado;Proceedings of the First Workshop on Hot Topics in Software Defined Networks,2012

2. Performance analysis of software-defined networking (SDN);Gelberger;Proceedings of the 2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems,2013

3. Feature-based comparison and selection of Software Defined Networking (SDN) controllers;Khondoker;Proceedings of the 2014 World Congress on Computer Applications and Information Systems (WCCAIS),2014

4. How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions

5. An intelligent SDN framework for 5G heterogeneous networks

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Monitoring of Fire within Forest Using Advanced Digital Differential Based Neural Network;2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE);2024-05-09

2. TITAN: Combining a bidirectional forwarding graph and GCN to detect saturation attack targeted at SDN;PLOS ONE;2024-04-26

3. Analysis of the Impacts of Flooding-Based DDoS Attacks on SDN-Enabled Cloud;Communications in Computer and Information Science;2024

4. AI and IoT-assisted Healthcare Practices for Smart Management During Global Pandemics;2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON);2023-12-01

5. An Investigation of the Most Effective Ways for Cluster Sensors to Save Energy in Underwater Networks;2023 3rd International Conference on Computing and Information Technology (ICCIT);2023-09-13

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3