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
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/12/28
Y1 - 2023/12/28
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
UR - http://www.scopus.com/inward/citedby.url?scp=85199979618&partnerID=8YFLogxK
U2 - 10.1109/EICEEAI60672.2023.10590313
DO - 10.1109/EICEEAI60672.2023.10590313
M3 - Conference proceeding (ISBN)
AN - SCOPUS:85199979618
T3 - 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023
BT - 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023
Y2 - 27 December 2023 through 28 December 2023
ER -