DeepMDR: A Deep-Learning-Assisted Control Plane System for Scalable, Protocol-Independent, and Multi-Domain Network Automation

Deyun Li; Hongqiang Fang; Xu Zhang; Jin Qi; Zuqing Zhu

doi:10.1109/mcom.001.2000717

Intrusion Detection System in Software-Defined Networks Using Machine Learning and Deep Learning Techniques –A Comprehensive Survey

10.36227/techrxiv.17153213.v1 ◽

2021 ◽

Author(s):

Md. Rayhan Ahmed ◽

salekul Islam ◽

Swakkhar Shatabda ◽

A. K. M. Muzahidul Islam ◽

Md. Towhidul Islam Robin

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Software Defined Networks ◽

Control Plane ◽

Data Plane ◽

Benchmark Datasets ◽

Comprehensive Survey

<div>At present, the Internet is facing numerous attacks of different kinds that put its data at risk. The safety of information within the network is, therefore, a significant concern. In order to prevent the loss of incredibly valuable information, the Intrusion Detection System (IDS) was developed to recognize the outbreak of a stream of attacks and notify the network system administrator providing network security. IDS is an extrapolative model used to detect network traffic as routine or attack. Software-Defined Networks (SDN) is a revolutionary paradigm that isolates the control plane from the data plane, transforming the concept of a software-driven network. Through this data and control plane separation, SDN provides us the opportunity to create a manageable and programmable network, allowing applications in the top plane to access physical devices via the controller. The controller functioning inside the control plane executes network modules and establishes flow rules to forward packets in the switches residing in the data plane. Cyber attackers target the SDN controller to subdue the control plane, which is considered the brain of the SDN, providing a plethora of functionalities such as regulating flow control to switches or routers in the data plane below via southbound Application Programming Interfaces (APIs) and business and application logic in the application plane above via northbound APIs to implement sophisticated networks. However, the control plane becomes a tempting prospect for security attacks from adversaries because of its centralization feature. This paper includes an in-depth overview of the notable published articles from 2015 to 2021 that used Machine Learning (ML) and Deep Learning (DL) techniques to construct an IDS solution to provide security for SDN. We also present two detailed taxonomic studies regarding IDS, and ML-DL techniques based on their learning categories, exploring various IDS solutions to secure the SDN paradigm. We have also conducted brief research on a few benchmark datasets used to construct IDS in the SDN paradigm. To conclude the survey, we provide a discussion that sheds light on continuous challenges and IDS issues for SDN security.</div>

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Deep Learning SDN Intrusion Detection Scheme Based on TW-Pooling

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2019.p0396 ◽

2019 ◽

Vol 23 (3) ◽

pp. 396-401

Author(s):

Qingyue Meng ◽

Shihui Zheng ◽

Yongmei Cai ◽

◽

Keyword(s):

Deep Learning ◽

Intrusion Detection ◽

Attack Detection ◽

Numerical Control ◽

Software Defined Network ◽

Traffic Data ◽

Control Plane ◽

Data Set ◽

Detection Scheme ◽

Entire Network

The numerical control separation in the Software-Defined Network (SDN) allows the control plane to have the absolute management rights of the network. As a new management plane of the SDN, once it is attacked, it will cause the entire network to face flaws. For this reason, this paper proposes a SDN control plane attack detection scheme based on deep learning, which can detect and respond to attacks on the SDN control plane in time. In this scenario, we propose a new pooling scheme that uses the TF-IDF idea to weight the characteristics of network traffic. Ultimately, our method achieved an accuracy of 99.8% in the SDN network’s traffic data set including 24 attack types.

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Intrusion Detection System in Software-Defined Networks Using Machine Learning and Deep Learning Techniques –A Comprehensive Survey

10.36227/techrxiv.17153213 ◽

2021 ◽

Author(s):

Md. Rayhan Ahmed ◽

salekul Islam ◽

Swakkhar Shatabda ◽

A. K. M. Muzahidul Islam ◽

Md. Towhidul Islam Robin

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Software Defined Networks ◽

Control Plane ◽

Data Plane ◽

Benchmark Datasets ◽

Comprehensive Survey

<div>At present, the Internet is facing numerous attacks of different kinds that put its data at risk. The safety of information within the network is, therefore, a significant concern. In order to prevent the loss of incredibly valuable information, the Intrusion Detection System (IDS) was developed to recognize the outbreak of a stream of attacks and notify the network system administrator providing network security. IDS is an extrapolative model used to detect network traffic as routine or attack. Software-Defined Networks (SDN) is a revolutionary paradigm that isolates the control plane from the data plane, transforming the concept of a software-driven network. Through this data and control plane separation, SDN provides us the opportunity to create a manageable and programmable network, allowing applications in the top plane to access physical devices via the controller. The controller functioning inside the control plane executes network modules and establishes flow rules to forward packets in the switches residing in the data plane. Cyber attackers target the SDN controller to subdue the control plane, which is considered the brain of the SDN, providing a plethora of functionalities such as regulating flow control to switches or routers in the data plane below via southbound Application Programming Interfaces (APIs) and business and application logic in the application plane above via northbound APIs to implement sophisticated networks. However, the control plane becomes a tempting prospect for security attacks from adversaries because of its centralization feature. This paper includes an in-depth overview of the notable published articles from 2015 to 2021 that used Machine Learning (ML) and Deep Learning (DL) techniques to construct an IDS solution to provide security for SDN. We also present two detailed taxonomic studies regarding IDS, and ML-DL techniques based on their learning categories, exploring various IDS solutions to secure the SDN paradigm. We have also conducted brief research on a few benchmark datasets used to construct IDS in the SDN paradigm. To conclude the survey, we provide a discussion that sheds light on continuous challenges and IDS issues for SDN security.</div>

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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