BMC-SDN: Blockchain-Based Multicontroller Architecture for Secure Software-Defined Networks

Multicontroller software-defined networks have been widely adopted to enable management of large-scale networks. However, they are vulnerable to several attacks including false data injection, which creates topology inconsistency among controllers. To deal with this issue, we propose BMC-SDN, a security architecture that integrates blockchain and multicontroller SDN and divides the network into several domains. Each SDN domain is managed by one master controller that communicates through blockchain with the masters of the other domains. The master controller creates blocks of network flow updates, and its redundant controllers validate the new block based on a proposed reputation mechanism. The reputation mechanism rates the controllers, i.e., block creator and voters, after each voting operation using constant and combined adaptive fading reputation strategies. The evaluation results demonstrate a fast and optimal detection of fraudulent flow rule injection.

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InSight2: A Modular Visual Analysis Platform for Network Situational Awareness in Large-Scale Networks

Electronics ◽

10.3390/electronics9101747 ◽

2020 ◽

Vol 9 (10) ◽

pp. 1747

Author(s):

Hansaka Angel Dias Edirisinghe Kodituwakku ◽

Alex Keller ◽

Jens Gregor

Keyword(s):

Real Time ◽

Computer Networks ◽

Network Flow ◽

Large Scale ◽

Visual Analysis ◽

Situational Awareness ◽

Monitoring Network ◽

Network Activity ◽

Flow Data ◽

Large Scale Networks

The complexity and throughput of computer networks are rapidly increasing as a result of the proliferation of interconnected devices, data-driven applications, and remote working. Providing situational awareness for computer networks requires monitoring and analysis of network data to understand normal activity and identify abnormal activity. A scalable platform to process and visualize data in real time for large-scale networks enables security analysts and researchers to not only monitor and study network flow data but also experiment and develop novel analytics. In this paper, we introduce InSight2, an open-source platform for manipulating both streaming and archived network flow data in real time that aims to address the issues of existing solutions such as scalability, extendability, and flexibility. Case-studies are provided that demonstrate applications in monitoring network activity, identifying network attacks and compromised hosts and anomaly detection.

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Bayesian dynamic modeling and monitoring of network flows

Network Science ◽

10.1017/nws.2019.10 ◽

2019 ◽

Vol 7 (3) ◽

pp. 292-318 ◽

Cited By ~ 2

Author(s):

Xi Chen ◽

David Banks ◽

Mike West

Keyword(s):

Time Series ◽

Dynamic Modeling ◽

Network Flow ◽

Large Scale ◽

Sequential Analysis ◽

Linear Models ◽

Network Flows ◽

Dynamic Network ◽

Dynamic Network Flow ◽

Large Scale Networks

AbstractIn the context of a motivating study of dynamic network flow data on a large-scale e-commerce website, we develop Bayesian models for online/sequential analysis for monitoring and adapting to changes reflected in node–node traffic. For large-scale networks, we customize core Bayesian time series analysis methods using dynamic generalized linear models (DGLMs). These are integrated into the context of multivariate networks using the concept of decouple/recouple that was recently introduced in multivariate time series. This method enables flexible dynamic modeling of flows on large-scale networks and exploitation of partial parallelization of analysis while maintaining coherence with an over-arching multivariate dynamic flow model. This approach is anchored in a case study on Internet data, with flows of visitors to a commercial news website defining a long time series of node–node counts on over 56,000 node pairs. Central questions include characterizing inherent stochasticity in traffic patterns, understanding node–node interactions, adapting to dynamic changes in flows and allowing for sensitive monitoring to flag anomalies. The methodology of dynamic network DGLMs applies to many dynamic network flow studies.

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of Brain Cells & Circuits “Camillo Golgi”: The Neural Bases of Action – from cellular microcircuits to large-scale networks and modelling

School of Brain Cells & Circuits “Camillo Golgi”: The Neural Bases of Action – from cellular microcircuits to large-scale networks and modelling ◽

10.3389/978-2-88963-087-5 ◽

2019 ◽

Keyword(s):

Large Scale ◽

Brain Cells ◽

Neural Bases ◽

Large Scale Networks ◽

Camillo Golgi

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Adaptation of Random Binomial Graphs for Testing Network Flow Problems Algorithms

Mathematics ◽

10.3390/math9151716 ◽

2021 ◽

Vol 9 (15) ◽

pp. 1716

Author(s):

Adrian Marius Deaconu ◽

Delia Spridon

Keyword(s):

Network Flow ◽

Large Scale ◽

Random Network ◽

Minimum Cost ◽

Maximum Flow ◽

Network Flow Problems ◽

Commodity Flow ◽

Vast Number ◽

Flow Problems ◽

Execution Speed

Algorithms for network flow problems, such as maximum flow, minimum cost flow, and multi-commodity flow problems, are continuously developed and improved, and so, random network generators become indispensable to simulate the functionality and to test the correctness and the execution speed of these algorithms. For this purpose, in this paper, the well-known Erdős–Rényi model is adapted to generate random flow (transportation) networks. The developed algorithm is fast and based on the natural property of the flow that can be decomposed into directed elementary s-t paths and cycles. So, the proposed algorithm can be used to quickly build a vast number of networks as well as large-scale networks especially designed for s-t flows.

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Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

Cerebral Cortex ◽

10.1093/cercor/bhab007 ◽

2021 ◽

Author(s):

Miguel Dasilva ◽

Christian Brandt ◽

Marc Alwin Gieselmann ◽

Claudia Distler ◽

Alexander Thiele

Keyword(s):

Attentional Control ◽

Large Scale ◽

Neuronal Excitability ◽

Cell Types ◽

Receptor Activation ◽

Frontal Eye Field ◽

Control Signals ◽

Cognitive Operations ◽

Eye Field ◽

Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

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Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15 ◽

10.1145/2783258.2783385 ◽

2015 ◽

Cited By ~ 26

Author(s):

Michael Mitzenmacher ◽

Jakub Pachocki ◽

Richard Peng ◽

Charalampos Tsourakakis ◽

Shen Chen Xu

Keyword(s):

Large Scale ◽

Large Scale Networks

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How does the position of firms in the supply chain affect their performance? An empirical study

Applied Network Science ◽

10.1007/s41109-021-00364-9 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Siddharth Arora ◽

Alexandra Brintrup

Keyword(s):

Supply Chain ◽

Empirical Data ◽

Large Scale ◽

Individual Performance ◽

Supply Network ◽

Supply Chain Networks ◽

Focal Firm ◽

Cooperative Competition ◽

Large Scale Networks ◽

The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

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