research-article

Machine Learning approach to Secure Software Defined Network: Machine Learning and Artificial Intelligence

Authors:

AfafD. Althobiti,

RababM. Almohayawi,

OmaimahO. BamsagAuthors Info & Claims

ICFNDS '20: Proceedings of the 4th International Conference on Future Networks and Distributed Systems

Article No.: 6, Pages 1 - 8

https://doi.org/10.1145/3440749.3442597

Published: 13 May 2021 Publication History

Abstract

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.

References

[1]

Cox, Jacob H., Advancing Software-Defined Networks A Survey. 2017. IEEE.25487- 25526.

[2]

Shin, Guofei Gu and Seungwon Shin. 2013. Attacking Software-Defined Networks A First Feasibility Study.

[3]

Rana, Deepak Singh. 2019. Shiv Ashish Dhondiyal and Sushil Kumar Chamoli. Software Defined Networking (SDN) Challenges, issues and Solution. International Journal of Computer Sciences and Engineering,884-889.

[4]

Bijone, Manu. 2016. A Survey on Secure Network: Intrusion Detection & Prevention Approaches. American Journal of Information Systems,69-88.

[5]

Ghosh, Uttam, Pushpita Chatterjee and Sachin Shetty. 2017. A Security Framework for SDN-Enabled Smart Power Grids. IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW).

[6]

Priyadarshini, Rojalina and Rabindra Kumar Barik. 2019. A deep learning based intelligent framework to mitigate DDoS attack in fog environment. Journal of King Saud University - Computer and Information Sciences, 859-868.

[7]

Cepheli, Özge, Saliha, Büyükçorak and Güneş Karabulut Kurt. 2016. Hybrid Intrusion Detection System for DDoS Attacks. Journal of Electical and Computer Engineering.

Digital Library

[8]

Xiaoqiong, XU, YU Hongfang and YANG Kun. 2017. DDoS Attack in Software Defined Networks: A Survey. School of Communication & Information Engineering, University of Electronic Science and Technology of China.

[9]

Nam, Kiho and Keecheon Kim. 2018. A Study on SDN security enhancement using open source IDS/IPS Suricata. International Conference on Information and Communication Technology Convergence (ICTC).

[10]

Firnsy/Barnyard2. Github, 2020 from https://github.com/firnsy/barnyard2.

[11]

NSL-KDD Datasets Research Canadian Institute for Cybersecurity UNB. Unb.Ca, 2020 from https://www.unb.ca/cic/datasets/nsl.html.

[12]

DDoS Evaluation Dataset (CICDDoS2019) Datasets Research Canadian Institute for Cybersecurity UNB. Unb.Ca, 2020 from https://www.unb.ca/cic/datasets/ddos-2019.html.

[13]

Team, Mininet. "Mininet: An Instant Virtual Network on Your Laptop (Or Other PC) - Mininet". Mininet.Org, 2020, http://mininet.org/.

[14]

Network Traffic Flow Analyzer. Netflowmeter.Ca, 2020, http://www.netflowmeter.ca/netflowmeter.html.

[15]

Weka 3 - Data Mining with Open Source Machine Learning Software in Java". Cs. Waikato.Ac. Nz, 2020 from https://www.cs.waikato.ac.nz/ml/weka/.

[16]

Chi-Chung Chang and Jen Lin, "LIBSVM – A Library for Support Vector Machines". Csie.Ntu. Edu.Tw, 2020, https://www.csie.ntu.edu.tw/∼cjlin/libsvm/.

[17]

Polat, Huseyin. 2020. Detecting DDoS Attacks in Software-Defined Networks Through Feature Selection Methods and Machine Learning Models. Onur Polat and Aydin Cetin. 2020. Sustainability,1035.

[18]

Stephen Northcutt, Judy Novak, 2003. Network Intrusion Detection, third ed. New Riders Publishing, Indianapolis, pp. P79, P401–404.

Cited By

Khalid HAldabagh N(2024)A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking EnvironmentsEngineering, Technology & Applied Science Research10.48084/etasr.675614:2(13190-13200)Online publication date: 2-Apr-2024
https://doi.org/10.48084/etasr.6756
S CJustin Thangaraj S(2023)Data-driven ML Approaches for the concept of Self-healing in CWN, Including its Challenges and Possible Solutions2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)10.1109/ICONSTEM56934.2023.10142451(1-7)Online publication date: 6-Apr-2023
https://doi.org/10.1109/ICONSTEM56934.2023.10142451

Index Terms

Machine Learning approach to Secure Software Defined Network: Machine Learning and Artificial Intelligence

Index terms have been assigned to the content through auto-classification.

Recommendations

Analysis of Machine Learning for Securing Software-Defined Networking
Abstract
Advancements in Software-Defined Networking (SDN) are growing with the emerging network architectures which face the security issues and challenges when adversary launches severe attacks such as Distributed Denial of Service (DDoS) attacks. ...
Impact of DoS Attack in Software Defined Network for Virtual Network

A virtual network is a computer network which does not contain any physical link between two computational nodes instead they connect through the virtual links. In recent years the virtual network is managed by Software Defined Network (SDN). SDN is one ...
Machine Learning and Software Defined Network to secure communications in a swarm of drones
Abstract
As drones become more and more frequent in industry and perhaps tomorrow in everyday life, the variety and sensitivity of their missions will increase. Securing the communication taking place with the drones and especially in the network of a ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICFNDS '20: Proceedings of the 4th International Conference on Future Networks and Distributed Systems

November 2020

313 pages

ISBN:9781450388863

DOI:10.1145/3440749

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Research
Refereed limited

Conference

ICFNDS '20

ICFNDS '20: The 4th International Conference on Future Networks and Distributed Systems

November 26 - 27, 2020

St.Petersburg, Russian Federation

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
71
Total Downloads

Downloads (Last 12 months)20
Downloads (Last 6 weeks)2

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Khalid HAldabagh N(2024)A Survey on the Latest Intrusion Detection Datasets for Software Defined Networking EnvironmentsEngineering, Technology & Applied Science Research10.48084/etasr.675614:2(13190-13200)Online publication date: 2-Apr-2024
https://doi.org/10.48084/etasr.6756
S CJustin Thangaraj S(2023)Data-driven ML Approaches for the concept of Self-healing in CWN, Including its Challenges and Possible Solutions2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)10.1109/ICONSTEM56934.2023.10142451(1-7)Online publication date: 6-Apr-2023
https://doi.org/10.1109/ICONSTEM56934.2023.10142451

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents