Search Paper
  • Home
  • Login
  • Categories
  • Post URL
  • Academic Resources
  • Contact Us

 

An Efficient Deep Learning Approach for Network Intrusion Detection System on Software Defined Network

google+
Views: 9                 

Author :  Mhmood Radhi Hadi

Affiliation :  Karabuk University

Country :  Turkey

Category :  Networks & Communications

Volume, Issue, Month, Year :  14, 4, July, 2022

Abstract :


Software-defined Networking (SDN) is a new technology for changing network architecture and making it more flexible and controllable. SDN can control all tasks of a network through the controller. Providing security for controller consider extremely important. Due side of the controller on the network side Network intrusion detection system (NIDS) will be effective to provide security for the controller. In this study, we suggest building a system (NIDS-DL) to detect attacks using 5 deep learning classifiers (DNN, CNN, RNN, LSTM, GRU). Our approach depends on the binary classification of the attacks. We used the NSL-KDD dataset in our study to train our deep learning classifiers. We employed 12 features extracted from 41 features using the feature selection method. CNN classifiers harvest the highest results in most evaluation metrics. Other classifiers also achieved good results. We compared our deep learning classifiers with each other and with other related studies. Our approach achieved success in identifying the attacks and might be used with great efficiency in the future.

Keyword :  Network Intrusion Detection System, Software Defined Networking, Deep Learning, NIDS, SDN

URL :  https://aircconline.com/ijnsa/V14N4/14422ijnsa01.pdf

User Name : Brendon Clarke
Posted 03-08-2022 on 14:20:12 AEDT



Related Research Work

  • Self-protection Mechanism For Wireless Sensor Networks
  • Human Mobility Patterns Modelling Using Cdrs
  • Effects Of Mac Parameters On The Performance Of Ieee 802.11 Dcf In Ns-3
  • Adaptive Array Beamforming Using An Enhanced Rls Algorithm

About Us | Post Cfp | Share URL Main | Share URL category | Post URL
All Rights Reserved @ Call for Papers - Conference & Journals