2021 International Conference on Electronic Engineering (ICEEM)
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An Efficient Intrusion Detection System for Software Defined Networking using Convolutional Neural Network
Oral Presentation , Page 210-214 (5) XML
Volume Title: 2nd IEEE International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 3-4 July. 2021
Authors
Faculty of electronic engineering
Abstract
With the accelerated development of computer networks utilization and the enormous growth of the number of applications running on top of it, network security becomes more significant. Intrusion Detection Systems (IDS) is considered as one of the essential tools utilized to protect computer networks and information systems. Software-defined network (SDN) architecture is used to provide network monitoring and analysis mechanism due to the programming environment of the SDN controller. On the other hand intrusion detection system is developed to monitor incoming traffic to the SDN network; hence it enables SDN to adjust security service insertion. In this paper, an efficient intrusion detection system using CNN is proposed and applied on a new attack-specific SDN dataset called InSDN. The proposed model is outperformed in compared with different machine learning algorithms such as CART, LR, LDA, SVM, NB and AB.
Keywords