scholarly journals A Topology Visualization Early Warning Distribution Algorithm for Large-Scale Network Security Incidents

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Hui He ◽  
Guotao Fan ◽  
Jianwei Ye ◽  
Weizhe Zhang
Keyword(s):  
Network Security ◽  
Network Topology ◽  
Early Warning ◽  
Early Warning System ◽  
Large Scale ◽  
Warning System ◽  
Small Scale ◽  
Large Scale Network ◽  
Scale Network ◽  
Security Incidents

It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system’s emergency response capabilities, alleviate the cyber attacks’ damage, and strengthen the system’s counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system’s plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks’ topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

A Novel Predication Approach for Network Security Situation Inspired by Immunity

2010 ◽  
Vol 20-23 ◽  
pp. 849-855 ◽  
Author(s):  
Yuan Quan Shi ◽  
Tao Li ◽  
Wen Chen ◽  
Rui Rui Zhang
Keyword(s):  
Network Security ◽  
Large Scale ◽  
Security Attacks ◽  
Evolution Operators ◽  
Large Scale Network ◽  
Novel Approach ◽  
Formal Definitions ◽  
The Future ◽  
Future Situation ◽  
Scale Network

To effectively prevent large-scale network security attacks, a novel Predication Approach for Network Security Situation inspired by Immunity (PANSSI) is proposed. In this predication approach, the concepts and formal definitions of antigen and antibody in the network security situation predication domain are given; meanwhile, the mathematical models of some antibody evolution operators being related to PANSSI are exhibited. By analyzing time series and computing the affinity between antigen and antibody in artificial immune system, network security situation predication model is established, and then the future situation of network security attacks is predicted by it. Experimental results prove that PANSSI can forecast the future network security situation real-timely and correctly, and provides a novel approach for network security situation predication.

Blueprints for a Large-Scale Early Warning System

2010 ◽  
Author(s):  
Paul G. Spirakis ◽  
Vasileios Vlachos ◽  
Vassilios Karakoidas ◽  
Dimitrios Liappis ◽  
Dimitrios Kalaitzis ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Large Scale ◽  
Warning System

scholarly journals Lightweight Label Propagation for Large-Scale Network Data

2018 ◽  
Author(s):  
De-Ming Liang ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Label Propagation ◽  
Stochastic Gradient Descent ◽  
Small Scale ◽  
Warm Start ◽  
Large Scale Data ◽  
Large Scale Network ◽  
Memory Overhead ◽  
Scale Network ◽  
Scale Data

Label propagation spreads the soft labels from few labeled data to a large amount of unlabeled data according to the intrinsic graph structure. Nonetheless, most label propagation solutions work under relatively small-scale data and fail to cope with many real applications, such as social network analysis, where graphs usually have millions of nodes. In this paper, we propose a novel algorithm named \algo to deal with large-scale data. A lightweight iterative process derived from the well-known stochastic gradient descent strategy is used to reduce memory overhead and accelerate the solving process. We also give a theoretical analysis on the necessity of the warm-start technique for label propagation. Experiments show that our algorithm can handle million-scale graphs in few seconds while achieving highly competitive performance with existing algorithms.

scholarly journals A Hierarchical and Abstraction-Based Blockchain Model

2019 ◽  
Vol 9 (11) ◽  
pp. 2343 ◽  
Author(s):  
Swagatika Sahoo ◽  
Akshay M. Fajge ◽  
Raju Halder ◽  
Agostino Cortesi
Keyword(s):  
Large Scale ◽  
Abstract Interpretation ◽  
Organizational Structures ◽  
Network Size ◽  
Small Scale ◽  
Time Interval ◽  
Huge Number ◽  
Large Scale Network ◽  
Regular Time ◽  
Scale Network

In the nine years since its launch, amid intense research, scalability is always a serious concern in blockchain, especially in case of large-scale network generating huge number of transaction-records. In this paper, we propose a hierarchical blockchain model characterized by: (1) each level maintains multiple local blockchain networks, (2) each local blockchain records local transactional activities, and (3) partial views (tunable w.r.t. precision) of different subsets of local blockchain-records are maintained in the blockchains at next level of the hierarchy. To meet this objective, we apply abstractions on a set of transaction-records in a regular time interval by following the Abstract Interpretation framework, which provides a tunable precision in various abstract domain and guarantees the soundness of the system. While this model suitably fits to the real-worlds organizational structures, the proposal is powerful enough to scale when large number of nodes participate in a network resulting into an enormous growth of the network-size and the number of transaction-records. We discuss experimental results on a small-scale network with three sub networks at lower-level and by abstracting the transaction-records in the abstract domain of intervals. The results are encouraging and clearly indicate the effectiveness of this approach to control exponential growth of blockchain size w.r.t. the total number of participants in the network.

scholarly journals Integrated framework for early warning system in UAE

2016 ◽  
Vol 7 (4) ◽  
pp. 361-373 ◽  
Author(s):  
Abdulla Ali Alhmoudi ◽  
Zeeshan Aziz
Keyword(s):  
Natural Hazards ◽  
Early Warning ◽  
Early Warning System ◽  
Large Scale ◽  
Warning System ◽  
Essential Elements ◽  
The Other ◽  
Content Type ◽  
Integrated Framework ◽  
The Impact

Purpose The impacts and costs of natural disasters on people, properties and environment are often severe when these occur on a large scale and with no warning system in place. The lack of deployment of an early warning system (EWS), low risk and hazard knowledge and impact of natural hazard experienced by some communities in the UAE have emphasised the need for more effective EWSs. This work focuses on developing an integrated framework for EWSs for communities prone to the impact of natural hazards to reduce their vulnerability and improve emergency management arrangements in the UAE. Design/methodology/approach The essential elements of effective EWS were identified through literature review to develop an integrated framework for EWS. Semi-structured interviews and questionnaires were also used to identify and confirm hindering factors to deployment of effective EWSs in Abu Dhabi and Fujairah Emirates, while areas that require further development were also identified through this means. Findings The outcome of this research revealed that the warning for natural hazards in the UAE lacked the required elements for effective EWS, whereas the elements which are present are insufficient to mitigate the impacts of natural hazards. The information in this work emphasises the need to improve two elements, and to develop the other two essential elements of EWS in the UAE. Originality/value The outcome of this research revealed that the warning for natural hazards in the UAE lacked the required elements for effective EWS, whereas the elements which are present are insufficient to mitigate the impacts of natural hazards. The information in this work emphasises the need to improve two elements and to develop the other two essential elements of EWS in the UAE.

Implementation of a paediatric early warning system as a complex health technology intervention

2020 ◽  
pp. archdischild-2020-318795
Author(s):  
Heather Duncan ◽  
Adrienne P Hudson
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Scale Up ◽  
Complex Interventions ◽  
Warning System ◽  
Early Warning Systems ◽  
Human Action ◽  
Small Scale ◽  
Warning Systems ◽  
National Implementation

The national implementation groups of early warning systems in the UK and Ireland have identified a need to understand implementation, adoption and maintenance of these complex interventions. The literature on how to implement, scale, spread and sustain these systems is sparse. We describe a successful adoption and maintenance over 10 years of a paediatric early warning system as a sociotechnical intervention using the Nonadoption, Abandonment, Challenges to the Scale-Up, Spread, and Sustainability Framework for Health and Care Technologies. The requirement for iterative processes within environment, culture, policy, human action and the wider system context may explain the possible reasons for improved outcomes in small-scale implementation and meta-analyses that are not reported in multicentre randomised control trials of early warning systems.

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