Machine Learning-Based Approach for Detecting DDoS Attack in SDN

Athari Alnatsheh; Ayoub Alsarhan; Mohammad Aljaidi; Husnain Rafiq; Khalid Mansour; Ghassan Samara; Bashar Igried; Yousef Ali Al Gumaei

doi:10.1109/eiceeai60672.2023.10590313

Machine Learning-Based Approach for Detecting DDoS Attack in SDN

Athari Alnatsheh, Ayoub Alsarhan, Mohammad Aljaidi, Husnain Rafiq, Khalid Mansour, Ghassan Samara, Bashar Igried, Yousef Ali Al Gumaei

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

With the great widespread networking and Software-Defined Network (SDN) solutions, software-defined networks have become the target of many different attacks and security threats. Software-defined networks are frequently exposed to denial-of-service attacks and distributed denial-of-service (DDoS), which may harm the controller or switch of SDN.. Consequently, the services offered by this network can be negatively affected.In this research, an experimental work was conducted to detect a DDoS Flooding attack. The features were extracted from a dataset to understand the behavior of the SDN and measure its performance in case of normally operating or when it is subjected to a DDoS attack.The performance of SDN was evaluated using several machine learning classifiers. Three classifiers are used in our experiments: Random forest (RF), Support vector machine (SVM), and Naive Bayes (NB).The results showed the superiority of the RF classifier over other classifiers with a detection accuracy of 98.89%.

Original language	English
Title of host publication	2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence 2023
Publisher	IEEE
ISBN (Electronic)	9798350373363
DOIs	https://doi.org/10.1109/eiceeai60672.2023.10590313
Publication status	Published - 18 Jul 2024
Event	2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence 2023 - Zarqa, Jordan Duration: 27 Dec 2023 → 28 Dec 2023

Conference

Conference	2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence 2023
Abbreviated title	EICEEAI 2023
Country/Territory	Jordan
City	Zarqa
Period	27/12/23 → 28/12/23

Keywords

Attack modelling
Distributed Denial of Service (DDoS)
Networks security
Software-defined networks (SDN)

ASJC Scopus subject areas

Artificial Intelligence
Energy Engineering and Power Technology
Health Informatics
Electrical and Electronic Engineering
Computer Vision and Pattern Recognition
Computer Science Applications

Access to Document

10.1109/eiceeai60672.2023.10590313

Cite this

@inproceedings{7b461e7443e3409b8bdccf0e5cd22f7a,

title = "Machine Learning-Based Approach for Detecting DDoS Attack in SDN",

abstract = "With the great widespread networking and Software-Defined Network (SDN) solutions, software-defined networks have become the target of many different attacks and security threats. Software-defined networks are frequently exposed to denial-of-service attacks and distributed denial-of-service (DDoS), which may harm the controller or switch of SDN.. Consequently, the services offered by this network can be negatively affected.In this research, an experimental work was conducted to detect a DDoS Flooding attack. The features were extracted from a dataset to understand the behavior of the SDN and measure its performance in case of normally operating or when it is subjected to a DDoS attack.The performance of SDN was evaluated using several machine learning classifiers. Three classifiers are used in our experiments: Random forest (RF), Support vector machine (SVM), and Naive Bayes (NB).The results showed the superiority of the RF classifier over other classifiers with a detection accuracy of 98.89%.",

keywords = "Attack modelling, Distributed Denial of Service (DDoS), Networks security, Software-defined networks (SDN)",

author = "Athari Alnatsheh and Ayoub Alsarhan and Mohammad Aljaidi and Husnain Rafiq and Khalid Mansour and Ghassan Samara and Bashar Igried and {Al Gumaei}, {Yousef Ali}",

year = "2024",

month = jul,

day = "18",

doi = "10.1109/eiceeai60672.2023.10590313",

language = "English",

booktitle = "2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence 2023",

publisher = "IEEE",

address = "United States",

note = "2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence 2023, EICEEAI 2023 ; Conference date: 27-12-2023 Through 28-12-2023",

}

Alnatsheh, A, Alsarhan, A, Aljaidi, M, Rafiq, H, Mansour, K, Samara, G, Igried, B & Al Gumaei, YA 2024, Machine Learning-Based Approach for Detecting DDoS Attack in SDN. in 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence 2023., 10590313, IEEE, 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence 2023, Zarqa, Jordan, 27/12/23. https://doi.org/10.1109/eiceeai60672.2023.10590313

TY - GEN

T1 - Machine Learning-Based Approach for Detecting DDoS Attack in SDN

AU - Alnatsheh, Athari

AU - Alsarhan, Ayoub

AU - Aljaidi, Mohammad

AU - Rafiq, Husnain

AU - Mansour, Khalid

AU - Samara, Ghassan

AU - Igried, Bashar

AU - Al Gumaei, Yousef Ali

PY - 2024/7/18

Y1 - 2024/7/18

N2 - With the great widespread networking and Software-Defined Network (SDN) solutions, software-defined networks have become the target of many different attacks and security threats. Software-defined networks are frequently exposed to denial-of-service attacks and distributed denial-of-service (DDoS), which may harm the controller or switch of SDN.. Consequently, the services offered by this network can be negatively affected.In this research, an experimental work was conducted to detect a DDoS Flooding attack. The features were extracted from a dataset to understand the behavior of the SDN and measure its performance in case of normally operating or when it is subjected to a DDoS attack.The performance of SDN was evaluated using several machine learning classifiers. Three classifiers are used in our experiments: Random forest (RF), Support vector machine (SVM), and Naive Bayes (NB).The results showed the superiority of the RF classifier over other classifiers with a detection accuracy of 98.89%.

AB - With the great widespread networking and Software-Defined Network (SDN) solutions, software-defined networks have become the target of many different attacks and security threats. Software-defined networks are frequently exposed to denial-of-service attacks and distributed denial-of-service (DDoS), which may harm the controller or switch of SDN.. Consequently, the services offered by this network can be negatively affected.In this research, an experimental work was conducted to detect a DDoS Flooding attack. The features were extracted from a dataset to understand the behavior of the SDN and measure its performance in case of normally operating or when it is subjected to a DDoS attack.The performance of SDN was evaluated using several machine learning classifiers. Three classifiers are used in our experiments: Random forest (RF), Support vector machine (SVM), and Naive Bayes (NB).The results showed the superiority of the RF classifier over other classifiers with a detection accuracy of 98.89%.

KW - Attack modelling

KW - Distributed Denial of Service (DDoS)

KW - Networks security

KW - Software-defined networks (SDN)

UR - http://www.scopus.com/inward/record.url?scp=85199979618&partnerID=8YFLogxK

U2 - 10.1109/eiceeai60672.2023.10590313

DO - 10.1109/eiceeai60672.2023.10590313

M3 - Conference contribution

BT - 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence 2023

PB - IEEE

T2 - 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence 2023

Y2 - 27 December 2023 through 28 December 2023

ER -

Machine Learning-Based Approach for Detecting DDoS Attack in SDN

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Keywords

ASJC Scopus subject areas

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