A Survey on Intrusion Detection System for Software Defined Networks (SDN)

2020 ◽  
Vol 16 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Yogita Hande ◽  
Akkalashmi Muddana
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Network Architecture ◽  
Detection System ◽  
Learning Approach ◽  
Distinctive Features ◽  
Detection Systems ◽  
Security Challenges ◽  
Machine Learning Approach

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.

A Survey on Intrusion Detection System for Software Defined Networks (SDN)

2021 ◽  
pp. 467-489
Author(s):  
Yogita Hande ◽  
Akkalashmi Muddana
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Network Architecture ◽  
Detection System ◽  
Learning Approach ◽  
Distinctive Features ◽  
Detection Systems ◽  
Security Challenges ◽  
Deep Learning Model

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.

scholarly journals A New Deep Learning Approach for Anomaly Base IDS using Memetic Classifier

2017 ◽  
Vol 12 (5) ◽  
pp. 677 ◽  
Author(s):  
Shahriar Mohammadi ◽  
Amin Namadchian
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Large Scale ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Learning Approach ◽  
Detection Systems ◽  
Video Mining ◽  
Proposed Model ◽  
Evaluation Algorithm

A model of an intrusion-detection system capable of detecting attack in computer networks is described. The model is based on deep learning approach to learn best features of network connections and Memetic algorithm as final classifier for detection of abnormal traffic.One of the problems in intrusion detection systems is large scale of features. Which makes typical methods data mining method were ineffective in this area. Deep learning algorithms succeed in image and video mining which has high dimensionality of features. It seems to use them to solve the large scale of features problem of intrusion detection systems is possible. The model is offered in this paper which tries to use deep learning for detecting best features.An evaluation algorithm is used for produce final classifier that work well in multi density environments.We use NSL-KDD and Kdd99 dataset to evaluate our model, our findings showed 98.11 detection rate. NSL-KDD estimation shows the proposed model has succeeded to classify 92.72% R2L attack group.

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