Intrusion Detection in Software Defined Network Using Deep Learning Approach

Bambang Susilo, Riri Fitri Sari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021
EditorsRajashree Paul
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages807-812
Number of pages6
ISBN (Electronic)9780738143941
DOIs
Publication statusPublished - 27 Jan 2021
Event11th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2021 - Virtual, Las Vegas, United States
Duration: 27 Jan 202130 Jan 2021

Publication series

Name2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021

Conference

Conference11th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2021
Country/TerritoryUnited States
CityVirtual, Las Vegas
Period27/01/2130/01/21

Keywords

  • DDoS
  • Deep Learning
  • Intrusion Detection
  • Machine Learning
  • SDN

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