Software Defined Network Detection System
Sang Boem Lim
Sang Boem Lim, Department of Smart ICT Convergence, Konkuk University, Seoul, Korea.
Manuscript received on 1 August 2019. | Revised Manuscript received on 6 August 2019. | Manuscript published on 30 September 2019. | PP: 1391-1395 | Volume-8 Issue-3 September 2019 | Retrieval Number: C3876098319/19©BEIESP | DOI: 10.35940/ijrte.B3549.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The ongoing increase in the use of wireless Internet and smartphones has resulted in changing consumer patterns, which has changed the demand for network usage such that existing hardware-centric devices cannot satisfy this demand. One of the fastest growing technologies is software define network, which can solve this problem. An intrusion detection system is a system that detects and responds to network attacks in real time in a network environment based on software define network. The focus of this paper is to present a deep learning-based network detection system. We describe pre-processing for deep learning algorithms and propose an architecture of the detection system. The analysis results of the system are also described.
Index Terms: Deep Learning, SDN, ONOS, Network Detection.
Scope of the Article: Systems and Software Engineering