92,367 research outputs found
Modeling Structure and Resilience of the Dark Network
While the statistical and resilience properties of the Internet are no more
changing significantly across time, the Darknet, a network devoted to keep
anonymous its traffic, still experiences rapid changes to improve the security
of its users. Here, we study the structure of the Darknet and we find that its
topology is rather peculiar, being characterized by non-homogenous distribution
of connections -- typical of scale-free networks --, very short path lengths
and high clustering -- typical of small-world networks -- and lack of a core of
highly connected nodes.
We propose a model to reproduce such features, demonstrating that the
mechanisms used to improve cyber-security are responsible for the observed
topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet
much more resilient than the Internet -- used as a benchmark for comparison at
a descriptive level -- to random failures, targeted attacks and cascade
failures, as a result of adaptive changes in response to the attempts of
dismantling the network across time.Comment: 8 pages, 5 figure
Robustness of scale-free networks to cascading failures induced by fluctuating loads
Taking into account the fact that overload failures in real-world functional
networks are usually caused by extreme values of temporally fluctuating loads
that exceed the allowable range, we study the robustness of scale-free networks
against cascading overload failures induced by fluctuating loads. In our model,
loads are described by random walkers moving on a network and a node fails when
the number of walkers on the node is beyond the node capacity. Our results
obtained by using the generating function method shows that scale-free networks
are more robust against cascading overload failures than Erd\H{o}s-R\'enyi
random graphs with homogeneous degree distributions. This conclusion is
contrary to that predicted by previous works which neglect the effect of
fluctuations of loads.Comment: 9 pages, 6 figure
Survivability in Time-varying Networks
Time-varying graphs are a useful model for networks with dynamic connectivity
such as vehicular networks, yet, despite their great modeling power, many
important features of time-varying graphs are still poorly understood. In this
paper, we study the survivability properties of time-varying networks against
unpredictable interruptions. We first show that the traditional definition of
survivability is not effective in time-varying networks, and propose a new
survivability framework. To evaluate the survivability of time-varying networks
under the new framework, we propose two metrics that are analogous to MaxFlow
and MinCut in static networks. We show that some fundamental
survivability-related results such as Menger's Theorem only conditionally hold
in time-varying networks. Then we analyze the complexity of computing the
proposed metrics and develop several approximation algorithms. Finally, we
conduct trace-driven simulations to demonstrate the application of our
survivability framework to the robust design of a real-world bus communication
network
On the Design of Clean-Slate Network Control and Management Plane
We provide a design of clean-slate control and management plane for data networks using the abstraction of 4D architecture, utilizing and extending 4D’s concept of a logically centralized Decision plane that is responsible for managing network-wide resources. In this paper, a scalable protocol and a dynamically adaptable algorithm for assigning Data plane devices to a physically distributed Decision plane are investigated, that enable a network to operate with minimal configuration and human intervention while providing optimal convergence and robustness against failures. Our work is especially relevant in the context of ISPs and large geographically dispersed enterprise networks. We also provide an extensive evaluation of our algorithm using real-world and artificially generated ISP topologies along with an experimental evaluation using ns-2 simulator
On the influence of topological characteristics on robustness of complex networks
In this paper, we explore the relationship between the topological
characteristics of a complex network and its robustness to sustained targeted
attacks. Using synthesised scale-free, small-world and random networks, we look
at a number of network measures, including assortativity, modularity, average
path length, clustering coefficient, rich club profiles and scale-free exponent
(where applicable) of a network, and how each of these influence the robustness
of a network under targeted attacks. We use an established robustness
coefficient to measure topological robustness, and consider sustained targeted
attacks by order of node degree. With respect to scale-free networks, we show
that assortativity, modularity and average path length have a positive
correlation with network robustness, whereas clustering coefficient has a
negative correlation. We did not find any correlation between scale-free
exponent and robustness, or rich-club profiles and robustness. The robustness
of small-world networks on the other hand, show substantial positive
correlations with assortativity, modularity, clustering coefficient and average
path length. In comparison, the robustness of Erdos-Renyi random networks did
not have any significant correlation with any of the network properties
considered. A significant observation is that high clustering decreases
topological robustness in scale-free networks, yet it increases topological
robustness in small-world networks. Our results highlight the importance of
topological characteristics in influencing network robustness, and illustrate
design strategies network designers can use to increase the robustness of
scale-free and small-world networks under sustained targeted attacks
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