Un plan de contrôle intelligent pour le déploiement de services de sécurité dans les réseaux SDN

2020 ◽  
pp. 29-67
Author(s):  
Maïssa MBAYE ◽  
Omessaad HAMDI
Keyword(s):  
Machine Learning ◽  
Software Defined Networking

L’approche SDN (Software-Defined Networking) consiste à piloter une infrastructure réseau par des applications logicielles. Au niveau des réseaux classiques, l’apprentissage machine (Machine Learning) et l’intelligence artificielle (IA) de manière générale ont montré leur efficacité pour la sécurité. La sécurité des réseaux SDN n’échappe pas à cette tendance et plusieurs travaux abordent déjà le problème de sécurité dans les réseaux SDN avec des solutions s’appuyant sur des outils de l’IA. La finalité ultime serait d’avoir des réseaux SDN intelligents capables de s’autoprotéger et s’auto-optimiser. L’objectif de ce chapitre est d’aborder des techniques de contrôle intelligent basées sur l’IA, afin de permettre un pilotage intelligent du déploiement de la sécurité.

An intelligent flow-based and signature-based IDS for SDNs using ensemble feature selection and a multi-layer machine learning-based classifier

2020 ◽  
pp. 1-20
Author(s):  
K. Muthamil Sudar ◽  
P. Deepalakshmi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Network Architecture ◽  
Detection System ◽  
Software Defined Networking ◽  
Control Plane ◽  
Control Logic ◽  
Data Plane

Software-defined networking is a new paradigm that overcomes problems associated with traditional network architecture by separating the control logic from data plane devices. It also enhances performance by providing a highly-programmable interface that adapts to dynamic changes in network policies. As software-defined networking controllers are prone to single-point failures, providing security is one of the biggest challenges in this framework. This paper intends to provide an intrusion detection mechanism in both the control plane and data plane to secure the controller and forwarding devices respectively. In the control plane, we imposed a flow-based intrusion detection system that inspects every new incoming flow towards the controller. In the data plane, we assigned a signature-based intrusion detection system to inspect traffic between Open Flow switches using port mirroring to analyse and detect malicious activity. Our flow-based system works with the help of trained, multi-layer machine learning-based classifier, while our signature-based system works with rule-based classifiers using the Snort intrusion detection system. The ensemble feature selection technique we adopted in the flow-based system helps to identify the prominent features and hasten the classification process. Our proposed work ensures a high level of security in the Software-defined networking environment by working simultaneously in both control plane and data plane.

scholarly journals Management of Software-Defined Networking Powered by Artificial Intelligence

2021 ◽  
Author(s):  
Jehad Ali ◽  
Byeong-hee Roh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Heterogeneous Networks ◽  
Software Defined Networking ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Underlying Network ◽  
And Control ◽  
The Internet Of Things

Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning.

scholarly journals Analysis of Machine Learning for Securing Software-Defined Networking

2021 ◽  
Vol 194 ◽  
pp. 229-236
Author(s):  
Hassan A. Alamri ◽  
Vijey Thayananthan
Keyword(s):  
Machine Learning ◽  
Software Defined Networking
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