TY - GEN
T1 - Lightweight Machine Learning Prediction Algorithm for Network Attack on Software Defined Network
AU - Ibrahimy, Arya Maulana
AU - Dewanta, Favian
AU - Aminanto, Muhammad Erza
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Nowadays, Software Defined Networking (SDN) technology is massively applied in network infrastructures. However, the SDN has several vulnerabilities to various network attacks, such as Denial of Service (DOS) for attacking the availability of the network and various web-based attacks, such as brute force and privilege escalation attacks. This research proposes a Machine Learning method to classify malicious traffic. The InSDN dataset will represent malicious traffic in the SDN environment. Feature correlation is used in this research to reduce the number of the features in InSDN dataset. The reduced dataset feature gives the fastest learning time with respect to the original dataset. The random forest gives the best metric with 99.9962% in accuracy with respect to learning methods, such as KNN and decision tree.
AB - Nowadays, Software Defined Networking (SDN) technology is massively applied in network infrastructures. However, the SDN has several vulnerabilities to various network attacks, such as Denial of Service (DOS) for attacking the availability of the network and various web-based attacks, such as brute force and privilege escalation attacks. This research proposes a Machine Learning method to classify malicious traffic. The InSDN dataset will represent malicious traffic in the SDN environment. Feature correlation is used in this research to reduce the number of the features in InSDN dataset. The reduced dataset feature gives the fastest learning time with respect to the original dataset. The random forest gives the best metric with 99.9962% in accuracy with respect to learning methods, such as KNN and decision tree.
KW - cybersecurity
KW - IDS
KW - machine learning
KW - SDN
UR - http://www.scopus.com/inward/record.url?scp=85147542488&partnerID=8YFLogxK
U2 - 10.1109/APWiMob56856.2022.10014244
DO - 10.1109/APWiMob56856.2022.10014244
M3 - Conference contribution
AN - SCOPUS:85147542488
T3 - APWiMob 2022 - Proceedings: 2022 IEEE Asia Pacific Conference on Wireless and Mobile
BT - APWiMob 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2022
Y2 - 9 December 2022 through 10 December 2022
ER -