scholarly journals Design of software-defined network experimental teaching scheme based on virtualised Environment

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Heng He ◽  
Yazhou Song ◽  
Tianzhe Xiao ◽  
Haseeb Ur Rehman ◽  
Lei Nie
Keyword(s):  
Large Scale ◽  
Attack Detection ◽  
Software Defined Network ◽  
Scheme Design ◽  
Malicious Attack ◽  
Experimental Scheme ◽  
Experimental Teaching ◽  
Experimental Environment ◽  
Flow Table ◽  
The Individual

Abstract Aiming to address the shortage of experimental resources, the high cost of large-scale deployment of hardware experimental environment and the difficulty for students to get started in the software-defined network (SDN) course, this article proposes an SDN experimental teaching scheme based on the virtualised environment, and gives a specific experimental scheme design. The scheme utilises virtualisation technology to build a SDN experimental environment quickly, uses a lightweight network simulation platform – that goes by the name of Mininet – to build the SDN network and uses open-source controller Floodlight for centralised control of the SDN network. The scheme is mainly divided into three phases: basic, improvement and synthesis. In the basic phase, experimental projects mainly include the study of SDN basic concepts and the use of relevant tools; in the improvement phase, experimental projects mainly include the use of SDN flow table, group table, etc; in the synthetic phase, we design two innovative experimental projects that use computational intelligence technology to achieve efficient load balancing and accurate malicious attack detection. The difficulty of each phase is increasing. The constantly evolving levels of difficulty allow the individual needs of students with different levels to be met, thereby improving the effect of SDN experimental teaching and cultivating innovative SDN talents.

scholarly journals A Table Overflow LDoS Attack Defending Mechanism in Software-Defined Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shengxu Xie ◽  
Changyou Xing ◽  
Guomin Zhang ◽  
Jinlong Zhao
Keyword(s):  
Large Scale ◽  
Defense Mechanism ◽  
Flow Analysis ◽  
Attack Detection ◽  
Large Scale Network ◽  
Flow Table ◽  
Small Flow ◽  
Scale Network ◽  
The Stability ◽  
Low Rate

In order to achieve requirements such as fast search of flow entries and mask matching, OpenFlow hardware switches usually use TCAM to store flow entries. Limited by the capacity of TCAM, the current commercial OpenFlow switches can only support hundreds of thousands of flow entries, which makes SDN network using OpenFlow hardware switches vulnerable to the threat of flow table overflow attack. Among them, low-rate DoS (LDoS) attack against table overflow poses a serious threat to SDN networks due to its high attack efficiency and concealed flow, and it is also difficult to detect. In this regard, this paper analyzed two types of LDoS attack flow against table overflow and proposed an attack detection and defense mechanism named SAIA (Small-flow Analysis and Inport-flow Analysis) through the design of table overflow prediction and flow entries deletion strategy. Experiments conducted through the SDN network environment showed that SAIA can effectively detect and suppress LDoS attack flows in the flow table in large-scale network conditions and verified that the deployment of SAIA is lightweight. At the same time, SAIA implemented the flow entry deletion strategy based on LRU when the flow table overflows in a nonattack situation, which further enhances the stability of the network.

scholarly journals A DDoS Attack Detection Method Based on SVM in Software Defined Network

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Jin Ye ◽  
Xiangyang Cheng ◽  
Jian Zhu ◽  
Luting Feng ◽  
Ling Song
Keyword(s):  
Learning Algorithm ◽  
Attack Detection ◽  
Attack Behavior ◽  
Software Defined Network ◽  
Attack Model ◽  
Ddos Attack ◽  
Deep Learning Algorithm ◽  
Average Accuracy ◽  
Flow Table ◽  
Neural Network Algorithm

The detection of DDoS attacks is an important topic in the field of network security. The occurrence of software defined network (SDN) (Zhang et al., 2018) brings up some novel methods to this topic in which some deep learning algorithm is adopted to model the attack behavior based on collecting from the SDN controller. However, the existing methods such as neural network algorithm are not practical enough to be applied. In this paper, the SDN environment by mininet and floodlight (Ning et al., 2014) simulation platform is constructed, 6-tuple characteristic values of the switch flow table is extracted, and then DDoS attack model is built by combining the SVM classification algorithms. The experiments show that average accuracy rate of our method is 95.24% with a small amount of flow collecting. Our work is of good value for the detection of DDoS attack in SDN.

DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment

2021 ◽  
Vol 15 (3) ◽  
pp. 1-18
Author(s):  
Hongsong Chen ◽  
Caixia Meng ◽  
Jingjiu Chen
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Network Traffic ◽  
Large Scale ◽  
Learning Algorithms ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Ddos Attack ◽  
Experimental Environment ◽  
Ddos Attack Detection

Aiming at the problem of DDoS attack detection in internet of things (IoT) environment, statistical and machine-learning algorithms are proposed to model and analyze the network traffic of DDoS attack. Docker-based virtualization platform is designed and configured to collect IoT network traffic data. Then the packet-level, flow-level, and second-level network traffic datasets are generated, and the importance of features in different traffic datasets are sorted. By SKlearn and TensorFlow machine-learning software framework, different machine learning algorithms are researched and compared. In packet-level DDoS attack detection, KNN algorithm achieves the best results; the accuracy is 92.8%. In flow-level DDoS attack detection, the voting algorithm achieves the best results; the accuracy is 99.8%. In second-level DDoS attack detection, the RNN algorithm behaves best results; the accuracy is 97.1%. The DDoS attack detection method combined with statistical analysis and machine-learning can effectively detect large-scale DDoS attacks on the internet of things simulation experimental environment.

Models, Practices and Methods of Reading: Evolution in Time and Space

2009 ◽  
pp. 59-64
Author(s):  
Yulia P. Melentyeva
Keyword(s):  
Large Scale ◽  
Social Groups ◽  
National Program ◽  
Time And Space ◽  
Lack Of Information ◽  
The Individual

In recent years as public in general and specialist have been showing big interest to the matters of reading. According to discussion and launch of the “Support and Development of Reading National Program”, many Russian libraries are organizing the large-scale events like marathons, lecture cycles, bibliographic trainings etc. which should draw attention of different social groups to reading. The individual forms of attraction to reading are used much rare. To author’s mind the main reason of such an issue has to be the lack of information about forms and methods of attraction to reading.

Flexibility Effects in a Single Link Robotic Manipulator

1993 ◽  
Author(s):  
C. Nataraj
Keyword(s):  
Large Scale ◽  
Robotic Manipulator ◽  
Flexible Beam ◽  
Rotating Speed ◽  
Weighted Residuals ◽  
Individual Interaction ◽  
Single Link ◽  
Rigid Mass ◽  
The Individual ◽  
Future Work

Abstract A single link robotic manipulator is modeled as a rotating flexible beam with a rigid mass at the tip and accurate energy expressions are derived. The resulting partial differential equations are solved using an approximate method of weighted residuals. From the solutions, coupling between axial and flexural deformations and the interactions with rigid body motions are rigorously analyzed. The emphasis in the current paper is not on an exhaustive analysis of existing systems but it is rather intended to compare and highlight the various flexibility effects in a relatively simple system. Hence, a nondimensional parametric analysis is performed to determine the effect of several parameters (including the rotating speed) on the errors and the individual interaction effects are discussed. Comparison with previous work in the field shows important phenomena often ignored or buried in large scale numerical analyses. Future work including application to multi-link robots is outlined.

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 HUMAN ENERGY UTILIZATION AND EFFECTIVENESS IN SMALL AND MARGINAL FARM HOLDINGS

2018 ◽  
Vol 6 (11) ◽  
pp. 163-171
Author(s):  
Tandra Mondal ◽  
Pranab Kumar Nag
Keyword(s):  
Large Scale ◽  
Energy Utilization ◽  
Hand Tools ◽  
Present Contribution ◽  
Land Preparation ◽  
Farm Work ◽  
Farm Mechanization ◽  
Power Tiller ◽  
Paddy Cultivation ◽  
The Individual

In India, small and marginal farmers have emerged as a distinct and dominant category. While farm mechanization represents a rapid transformation from traditional to modern methods of farming, it is not uniform across the crops and regions. The level of mechanization, however, remains scattered due to the compulsiveness to the situation dominated by the economic layout of farm holdings, land size, and large-scale deprivation of access to the technology suitable to small holdings. This present contribution elucidates the extent of use tools and machinery among the rice farmers of the state of Wes Bengal, India. Analysis revealed that the total number of man-days involved in paddy cultivation was 120-140 per ha, i.e., 900-1000 man-hours depending upon the availability of labour, tools, and machinery used for the individual operation. Analysis of farm work in small and marginal holdings evolved that over 90% of the total number of farmers use either tractor or power tiller for land preparation. Use of the animal-drawn country plough is gradually phased out in the study regions. For sowing and transplanting operations are primarily manual methods using hand tools. The study provided an insight of the issues of work methods and practices of the farmworkers in small and marginal farm holdings.

scholarly journals Making Soup: Preparing and Validating Molecular Simulations of the Bacterial Cytoplasm

2019 ◽  
Author(s):  
Leandro Oliveira Bortot ◽  
Zahedeh Bashardanesh ◽  
David van der Spoel
Keyword(s):  
Large Scale ◽  
Biological Properties ◽  
Computational Power ◽  
E Coli ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Computational Modeling And Simulation ◽  
Dynamics Simulations ◽  
The Individual ◽  
Bacterial Cytoplasm

Biomolecular crowding affects the biophysical and biochemical behavior of macro- molecules when compared to the dilute environment present in experiments made with isolated proteins. Computational modeling and simulation are useful tools to study how crowding affects the structural dynamics and biological properties of macromolecules. As computational power increased, modeling and simulating large scale all-atom explicit solvent models of the prokaryote cytoplasm become possible. In this work, we build an atomistic model of the cytoplasm of Escherichia coli composed of 1.5 million atoms and submit it to a total of 3 μs of molecular dynamics simulations. The properties of biomolecules under crowding conditions are compared to those from simulations of the individual compounds under dilute conditions. The simulation model is found to be consistent with experimental data about the diffusion coefficient and stability of macromolecules under crowded conditions. In order to stimulate further work we provide a Python script and a set of files that enables other researchers to build their own E. coli cytoplasm models to address questions related to crowding.<br>

scholarly journals Nonlinear visuoauditory integration in the mouse superior colliculus

2021 ◽  
Author(s):  
Shinya Ito ◽  
Yufei Si ◽  
Alan M. Litke ◽  
David A. Feldheim
Keyword(s):  
Superior Colliculus ◽  
Sensory Integration ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Critical Role ◽  
Sensory Information ◽  
Population Level ◽  
Object Identification ◽  
Response Properties ◽  
The Individual

AbstractSensory information from different modalities is processed in parallel, and then integrated in associative brain areas to improve object identification and the interpretation of sensory experiences. The Superior Colliculus (SC) is a midbrain structure that plays a critical role in integrating visual, auditory, and somatosensory input to assess saliency and promote action. Although the response properties of the individual SC neurons to visuoauditory stimuli have been characterized, little is known about the spatial and temporal dynamics of the integration at the population level. Here we recorded the response properties of SC neurons to spatially restricted visual and auditory stimuli using large-scale electrophysiology. We then created a general, population-level model that explains the spatial, temporal, and intensity requirements of stimuli needed for sensory integration. We found that the mouse SC contains topographically organized visual and auditory neurons that exhibit nonlinear multisensory integration. We show that nonlinear integration depends on properties of auditory but not visual stimuli. We also find that a heuristically derived nonlinear modulation function reveals conditions required for sensory integration that are consistent with previously proposed models of sensory integration such as spatial matching and the principle of inverse effectiveness.

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