Applying Formal Modelling to the Specification and Testing of SDN Network Functionality

<p>Software Defined Networks offers a new paradigm to manage networks, one that favors centralised control over the distributed control used in legacy networks. This brings network operators potential efficiencies in capital investment, operating costs and wider choice in network appliance providers. We explore in this research whether these efficiencies apply to all network functionality by applying formal modelling to create a mathematically rigourous model of a service, a firewall, and using that model to derive tests that are ultimately applied to two SDN firewalls and a legacy stateful firewall. In the process we discover the only publicly available examples of SDN firewalls are not equivalent to legacy stateful firewalls and in fact create a security flaw that may be exploited by an attacker.</p>

Applying Formal Modelling to the Specification and Testing of SDN Network Functionality

<p>Software Defined Networks offers a new paradigm to manage networks, one that favors centralised control over the distributed control used in legacy networks. This brings network operators potential efficiencies in capital investment, operating costs and wider choice in network appliance providers. We explore in this research whether these efficiencies apply to all network functionality by applying formal modelling to create a mathematically rigourous model of a service, a firewall, and using that model to derive tests that are ultimately applied to two SDN firewalls and a legacy stateful firewall. In the process we discover the only publicly available examples of SDN firewalls are not equivalent to legacy stateful firewalls and in fact create a security flaw that may be exploited by an attacker.</p>

Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

Oscillatory Neural Network (ONN) is an emerging neuromorphic architecture with oscillators representing neurons and information encoded in oscillator's phase relations. In an ONN, oscillators are coupled with electrical elements to define the network's weights and achieve massive parallel computation. As the weights preserve the network functionality, mapping weights to coupling elements plays a crucial role in ONN performance. In this work, we investigate relaxation oscillators based on VO2 material, and we propose a methodology to map Hebbian coefficients to ONN coupling resistances, allowing a large-scale ONN design. We develop an analytical framework to map weight coefficients into coupling resistor values to analyze ONN architecture performance. We report on an ONN with 60 fully-connected oscillators that perform pattern recognition as a Hopfield Neural Network.

A Resilience-Based Model for the Seismic Assessment of the Functionality of Road Networks Affected by Bridge Damage and Restoration

Road network functionality after an earthquake is a crucial aspect for an already struck community. In particular, bridges are susceptible to earthquake-induced damages and to lengthy restoration works. This may lead to severe and unexpected disruption of traffic. In this paper, a model for the assessment of the seismic resilience of a road network is presented. The proposed model permits us to evaluate the earthquake-induced perturbations to the functionality of a network in terms of transportation capacities, traffic congestion, and travel times due to bridge damages and subsequent restoration interventions. The evolution over time of the functionality of the network is studied by means of a multi-stage approach describing the evolution of the situation in terms of reducing the normal pre-earthquakes transportation capacities. The methodology has been illustrated with reference to a hypothetical case study, a road network composed of 14 nodes and 31 links.

Promoting Students' Health at University: Key Stakeholders, Cooperation, and Network Development

Background: Cooperation among university units is considered a cornerstone for the promotion of students' health. The underlying mechanisms of health-promoting networks at universities have rarely been examined so far. Shedding light on partnerships is generally limited to the naming of allied actors in a network.Objectives and Methods: In this study, we used network analysis intending to visualize and describe the positions and characteristics of the network actors, and examine organizational relationships to determine the characteristics of the complete network.Results: The network analysis at hand provides in-depth insights into university structures promoting students' health comprising 33 organizational units and hundreds of ties. Both cooperation and communication network show a flat, non-hierarchical structure, which is reflected by its low centralization indices (39–43%) and short average distances (1.43–1.47) with low standard deviations (0.499–0.507), small diameter (3), and the non-existence of subgroups. Density lies between 0.53 and 0.57. According to the respondents, the University Sports Center is considered the most important actor in the context of students' health. Presidium and Institute of Sport and Sports Science play an integral role in terms of network functionality.Conclusion: In the health-promoting network, numerous opportunities for further integration and interaction of actors exist. Indications for transferring results to other universities are discussed. Network analysis enables universities to profoundly analyze their health-promoting structures, which is the basis for sustained network governance and development.

‘End to end’ linkage structure for integrated impact assessment of infrastructure networks under natural hazards

An infrastructure impact assessment process relies on the analysis of multiple types of models, the performance of individual infrastructure networks and the interdependencies between multiple infrastructure networks. Several models are developed for their specific purposes and there is a need to link these models for the assessment of natural hazard impacts on distributed infrastructures to deliver the desired outcomes on network functionality and disruption levels that are suitable to assess socio-economic impact. In this paper, an ‘end-to-end’ linkage structure is proposed to link different models by which various features, data standards, parameters and structures are linked in a transparent and consistent manner. The framework has adopted a dedicated knowledge discovery and data analysis process to acquire information around input and output parameters for each of these models developed by various researchers and used in risk assessment tools. The framework is illustrated by applying the step-by-step procedure towards integrated impact assessments of electricity, potable water and road networks and their interdependencies.

Computational Efficiency of a Modular Reservoir Network for Image Recognition

Liquid state machine (LSM) is a type of recurrent spiking network with a strong relationship to neurophysiology and has achieved great success in time series processing. However, the computational cost of simulations and complex dynamics with time dependency limit the size and functionality of LSMs. This paper presents a large-scale bioinspired LSM with modular topology. We integrate the findings on the visual cortex that specifically designed input synapses can fit the activation of the real cortex and perform the Hough transform, a feature extraction algorithm used in digital image processing, without additional cost. We experimentally verify that such a combination can significantly improve the network functionality. The network performance is evaluated using the MNIST dataset where the image data are encoded into spiking series by Poisson coding. We show that the proposed structure can not only significantly reduce the computational complexity but also achieve higher performance compared to the structure of previous reported networks of a similar size. We also show that the proposed structure has better robustness against system damage than the small-world and random structures. We believe that the proposed computationally efficient method can greatly contribute to future applications of reservoir computing.

Introduction to Academy-Business Inter-Organizational Partnerships

This chapter explores organizational theory including inter-organizational behavior and several pro-social concerns for both individuals and organizations. A wide range of organizational theories support academy-business inter-organizational partnership functionality. Such theories include rational, natural, and open systems; identity and pro-social behavior; institutional theory; resource dependence theory; social exchange theory; stakeholder theory; and academic capitalism. Inter-organizational behavior is supported by network functionality as well as cross-sectional involvement by the federal and state governments. Inter-organizational partnerships are complex. Little research has been a focus specifically between higher education and companies. The academy-business inter-organizational partnership typology is introduced as a framework for exploring these relationships including concerns of philanthropic, transactional, symbiotic, and synergistic partnership dimensions.

Privacy Preserving Data Mining Using Time Series Data Aggregation

This article proposes a mechanism to provide privacy to mined results by assuming that the data is distributed across many nodes. The first objective includes mining the query results by the node in a cluster, communicating it to the cluster head, aggregating the data collected from all the cluster nodes and then communicating it to the group controller. The second objective is to incorporate privacy at each level of the clusters node: cluster head and the group controller level. The final objective is to provide a dynamic network feature, where the nodes can join or leave the distributed network without disturbing the network functionality. The proposed algorithm was implemented and validated in Java for its performance in terms of communication costs computational complexity.