@inproceedings{3a88635bc76f4e84b03ab002f42d37f2,
title = "Intrusion Detection in Software Defined Network Using Deep Learning Approach",
abstract = "The development of IoT technology and virtualization has made network management increasingly complex. Software-Defined Network has become a standard in virtualizing computer networks. With technology developing, more attacks on computer networks. Researchers have developed many ways to deal with attacks, one of the most developed methods is to use machine learning. To deal with attacks that occur needed new ways to deal with it. This research will discuss Software-Defined Network, attacks that can occur on SDN, propose flow traffic, and methods for classification of attacks using deep learning algorithms. This research uses the Python programming language, also utilized several packages such as pandas framework, NumPy, sci-kit learn, tensor flow, and seaborn. From the results of the study, it was found that the algorithm that had been developed could produce good accuracy with a different dataset.",
keywords = "DDoS, Deep Learning, Intrusion Detection, Machine Learning, SDN",
author = "Bambang Susilo and Sari, {Riri Fitri}",
note = "Funding Information: ACKNOWLEDGMENT We thank the University of Indonesia for financial support for this research under the PUTI Q2 Grant number NKB-1723/UN2.RST/HKP.05.00/2020. Publisher Copyright: {\textcopyright} 2021 IEEE.; 11th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2021 ; Conference date: 27-01-2021 Through 30-01-2021",
year = "2021",
month = jan,
day = "27",
doi = "10.1109/CCWC51732.2021.9375951",
language = "English",
series = "2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "807--812",
editor = "Rajashree Paul",
booktitle = "2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021",
address = "United States",
}