scholarly journals A Software-Defined Architecture for Integrating Heterogeneous Space and Ground Networks

2021 ◽  
Vol 2 ◽  
Author(s):  
Jie Sun ◽  
Feng Liu ◽  
Yong Li ◽  
Lianlian Zhang ◽  
Dingyuan Shi
Keyword(s):  
Heterogeneous Networks ◽  
Physical Layer ◽  
Collaborative Optimization ◽  
Software Defined Network ◽  
Multiple Networks ◽  
Data Plane ◽  
Efficient Control ◽  
Integrated Architecture ◽  
Multi Level ◽  
And Performance

In recent years, various types of heterogeneous networks develop rapidly. The integration of multi-type networks have great values in the fields of military and civil applications. The challenges of integrating multiple networks covers the heterogeneity of multiple aspects, e.g., the architectures, protocols, and switching mechanisms. The existing interconnection technologies of heterogeneous networks mainly include traditional static protocol gateways, traditional software-defined network (SDN) gateways, and improved SDN gateways. However, traditional static protocol gateways need to be customed in advance according to specific scenarios, which leads to the lack of flexibility. Traditional SDN gateways are often used for connecting homogeneous networks. The existing improved SDN gateways often neglect the efficiency and cost of integrating heterogeneous networks. In our work, we propose a software-defined architecture for integrating heterogeneous space and ground networks (SD-SGN). First, we propose an integrated architecture that utilizes SDN gateways and southbound interfaces to shield subnets’ heterogeneity ranging from the physical layer to the network layer. Second, we use the multi-class multi-level flow tables to provide a flexible data plane. Third, we offer an efficient control plane based on the subnet abstraction and global collaborative optimization. Fourth, we give a further discussion on customizing a complete network service based on the proposed SDN architecture. Last, extensive simulations demonstrate that this SDN architecture is effective and performs well in terms of costs, efficiency, and performance.

scholarly journals Unsupervised Multi-Level Feature Extraction for Improvement of Hyperspectral Classification

2021 ◽  
Vol 13 (8) ◽  
pp. 1602
Author(s):  
Qiaoqiao Sun ◽  
Xuefeng Liu ◽  
Salah Bourennane
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Spatial Information ◽  
Hyperspectral Data ◽  
Great Promise ◽  
Learning Models ◽  
Single Level ◽  
Multiple Networks ◽  
Multi Level ◽  
Hyperspectral Classification

Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features.

scholarly journals A novel industrial control architecture based on Software-Defined Network

2018 ◽  
Vol 51 (7-8) ◽  
pp. 360-367
Author(s):  
Geng Liang ◽  
Wen Li
Keyword(s):  
Large Scale ◽  
Service Management ◽  
Virtual Machines ◽  
Industrial Process ◽  
Control Network ◽  
Software Defined Network ◽  
Intelligent Network ◽  
Industrial Control ◽  
Control Functions ◽  
And Performance

Traditionally, routers and other network devices encompass both data and control functions in most large enterprise networks, making it difficult to adjust the network infrastructure and operation to large-scale addition of end systems, virtual machines, and virtual networks in industrial comprehensive automation. A network organizing technique that has come to recent prominence is the Software-Defined Network (SDN). A novel SDN based industrial control network (SDNICN) was proposed in this paper. Intelligent network components are included in a SDNICN. Switches in SDNICN provided fundamental network interconnection for the whole industrial control network. Network controller is used for data transmission, forwarding and routing control between different layers. Service Management Center (SMC) is essentially responsible for managing various services used in industrial process control. SDNICN can not only greatly improve the flexibility and performance of industrial control network but also meet the intelligence and informatization of the future industry.

scholarly journals Security of Data with Enhanced Technique of AASR Protocol for Secure Crosslayer Routing in MANET

2020 ◽  
Vol 6 (6) ◽  
pp. 17-21
Author(s):  
Shifana Begum ◽  
Megha M Gamskar ◽  
Prakrithi Mogasale
Keyword(s):  
Power Control ◽  
Routing Protocol ◽  
Physical Layer ◽  
Cross Layer ◽  
Cross Layer Design ◽  
Transmission Power ◽  
Dos Attacks ◽  
Layered Architecture ◽  
And Performance ◽  
Reliable Path

MANET supports communication without any wired medium and with layered architecture. It does not uses any infrastructure support. Present alternative to the layered architecture is cross layer design approaches and the interaction between the layers is supported. The security of CLPC (Cross Layer Design Approach for Power control) routing protocol will be discussed in this paper. The transmission power and finding the effective route between source and destination can be improved by CLPC. The reliable path between the source and destination can be determined by RSS from the physical layer, but it is vulnerable to the DOS attacks. Here we propose a Secure cross layer power control protocol SCLPC to placate the attacks on CLPC. The SCLPC protocol provides better results and performance.

IMPLEMENTASI DAN ANALISIS EFEKTIVITAS FIREWALL PADA SOFTWARE DEFINED NETWORK BERBASIS OPENFLOW

2018 ◽  
Vol 4 (2) ◽  
pp. 46-57
Author(s):  
Fathul Muiin ◽  
Henry Saptono
Keyword(s):  
Software Defined Network ◽  
Control Plane ◽  
Flow Table ◽  
Data Plane

Penggunaan akses internet di dunia semakin berkembang, dan selaras dengan perkembangan teknologi jaringan komputer yang semakin kompleks. Oleh karena itu, keamanan data pada sebuah komputer menjadi salah satu bagian yang sangat penting dalam sebuah jaringan. Dan SDN merupakan sebuah solusi untuk menyediakan kebutuhan jaringan komputer saat ini. Software Defined Network (SDN) merupakan pendekatan pada teknologi jaringan yang melakukan penyederhanaan terhadap kontrol dan manajemen jaringan. Pada jaringan ini nantinya akan menggunakan protokol openflow, yang prinsip utamanya memisahkan fungsi control plane dan data plane pada perangkat. Kontrol jaringan pada sebuah controller bersifat programmable, jadi dengan adanya SDN maka jaringan akan mudah diatur dan lebih fleksibel. Implementasi dan analisis firewall ini menggunakan emulator mininet untuk membuat topologi jaringan yang sederhana. Dalam pengujian firewall menggunakan bahasa XML untuk implementasi aliran data, lalu menggunakan aplikasi postman sebagai alat untuk menambahkan flow table baru pada switch, dan controller yang digunakan adalah opendaylight.

Identified and engaged: A multi-level dynamic model of identification with the group and performance in collaborative learning

2020 ◽  
Vol 78 ◽  
pp. 101838 ◽  
Author(s):  
Petru L. Curşeu ◽  
Andrei Rusu ◽  
Laurenţiu P. Maricuţoiu ◽  
Delia Vîrgă ◽  
Silvia Măgurean
Keyword(s):  
Collaborative Learning ◽  
Dynamic Model ◽  
Multi Level ◽  
And Performance

scholarly journals Mitigation of DDoS attack instigated by compromised switches on SDN controller by analyzing the flow rule request traffic

2018 ◽  
Vol 7 (2.6) ◽  
pp. 46 ◽  
Author(s):  
Sanjeetha R ◽  
Shikhar Srivastava ◽  
Rishab Pokharna ◽  
Syed Shafiq ◽  
Dr Anita Kanavalli
Keyword(s):  
Network Architecture ◽  
Flow Rule ◽  
Software Defined Network ◽  
Ddos Attacks ◽  
Control Plane ◽  
Ddos Attack ◽  
Flow Table ◽  
Data Plane ◽  
Ddos Detection ◽  
Sdn Controller

Software Defined Network (SDN) is a new network architecture which separates the data plane from the control plane. The SDN controller implements the control plane and switches implement the data plane. Many papers discuss about DDoS attacks on primary servers present in SDN and how they can be mitigated with the help of controller. In our paper we show how DDoS attack can be instigated on the SDN controller by manipulating the flow table entries of switches, such that they send continuous requests to the controller and exhaust its resources. This is a new, but one of the possible way in which a DDoS attack can be performed on controller. We show the vulnerability of SDN for this kind of attack. We further propose a solution for mitigating it, by running a DDoS Detection module which uses variation of flow entry request traffic from all switches in the network to identify compromised switches and blocks them completely.

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