702 research outputs found
Resilience of the Critical Communication Networks Against Spreading Failures: Case of the European National and Research Networks
A backbone network is the central part of the communication network, which provides connectivity within the various systems across large distances. Disruptions in a backbone network would cause severe consequences which could manifest in the service outage on a large scale. Depending on the size and the importance of the network, its failure could leave a substantial impact on the area it is associated with. The failures of the network services could lead to a significant disturbance of human activities. Therefore, making backbone communication networks more resilient directly affects the resilience of the area. Contemporary urban and regional development overwhelmingly converges with the communication infrastructure expansion and their obvious mutual interconnections become more reciprocal.
Spreading failures are of particular interest. They usually originate in a single network segment and then spread to the rest of network often causing a global collapse. Two types of spreading failures are given focus, namely: epidemics and cascading failures. How to make backbone networks more resilient against spreading failures? How to tune the topology or additionally protect nodes or links in order to mitigate an effect of the potential failure? Those are the main questions addressed in this thesis.
First, the epidemic phenomena are discussed. The subjects of epidemic modeling and identification of the most influential spreaders are addressed using a proposed Linear Time-Invariant (LTI) system approach. Throughout the years, LTI system theory has been used mostly to describe electrical circuits and networks. LTI is suitable to characterize the behavior of the system consisting of numerous interconnected components. The results presented in this thesis show that the same mathematical toolbox could be used for the complex network analysis.
Then, cascading failures are discussed. Like any system which can be modeled using an interdependence graph with limited capacity of either nodes or edges, backbone networks are prone to cascades. Numerical simulations are used to model such failures. The resilience of European National Research and Education Networks (NREN) is assessed, weak points and critical areas of the network are identified and the suggestions for its modification are proposed
Smart Grid Security: Threats, Challenges, and Solutions
The cyber-physical nature of the smart grid has rendered it vulnerable to a
multitude of attacks that can occur at its communication, networking, and
physical entry points. Such cyber-physical attacks can have detrimental effects
on the operation of the grid as exemplified by the recent attack which caused a
blackout of the Ukranian power grid. Thus, to properly secure the smart grid,
it is of utmost importance to: a) understand its underlying vulnerabilities and
associated threats, b) quantify their effects, and c) devise appropriate
security solutions. In this paper, the key threats targeting the smart grid are
first exposed while assessing their effects on the operation and stability of
the grid. Then, the challenges involved in understanding these attacks and
devising defense strategies against them are identified. Potential solution
approaches that can help mitigate these threats are then discussed. Last, a
number of mathematical tools that can help in analyzing and implementing
security solutions are introduced. As such, this paper will provide the first
comprehensive overview on smart grid security
Modeling Cascading Failures in the North American Power Grid
The North American power grid is one of the most complex technological
networks, and its interconnectivity allows both for long-distance power
transmission and for the propagation of disturbances. We model the power grid
using its actual topology and plausible assumptions about the load and overload
of transmission substations. Our results indicate that the loss of a single
substation can lead to a 25% loss of transmission efficiency by triggering an
overload cascade in the network. We systematically study the damage inflicted
by the loss of single nodes, and find three universal behaviors, suggesting
that 40% of the transmission substations lead to cascading failures when
disrupted. While the loss of a single node can inflict substantial damage,
subsequent removals have only incremental effects, in agreement with the
topological resilience to less than 1% node loss.Comment: 6 pages, 6 figure
Topology and congestion invariant in global internet-scale networks
PhDInfrastructures like telecommunication systems, power transmission
grids and the Internet are complex networks that are vulnerable to
catastrophic failure. A common mechanism behind this kind of failure
is avalanche-like breakdown of the network's components. If a
component fails due to overload, its load will be redistributed, causing
other components to overload and fail. This failure can propagate
throughout the entire network. From studies of catastrophic failures in
di erent technological networks, the consensus is that the occurrence
of a catastrophe is due to the interaction between the connectivity
and the dynamical behaviour of the networks' elements.
The research in this thesis focuses particularly on packet-oriented networks.
In these networks the tra c (dynamics) and the topology
(connectivity) are coupled by the routing mechanisms. The interactions
between the network's topology and its tra c are complex as
they depend on many parameters, e.g. Quality of Service, congestion
management (queuing), link bandwidth, link delay, and types of
tra c. It is not straightforward to predict whether a network will
fail catastrophically or not. Furthermore, even if considering a very
simpli ed version of packet networks, there are still fundamental questions
about catastrophic behaviour that have not been studied, such
as: will a network become unstable and fail catastrophically as its size
increases; do catastrophic networks have speci c connectivity properties?
One of the main di culties when studying these questions is that,
in general, we do not know in advance if a network is going to fail
catastrophically. In this thesis we study how to build catastrophic
5
networks. The motivation behind the research is that once we have
constructed networks that will fail catastrophically then we can study
its behaviour before the catastrophe occurs, for example the dynamical
behaviour of the nodes before an imminent catastrophe.
Our theoretical and algorithmic approach is based on the observation
that for many simple networks there is a topology-tra c invariant for
the onset of congestion. We have extended this approach to consider
cascading congestion. We have developed two methods to construct
catastrophes. The main results in this thesis are that there is a family
of catastrophic networks that have a scale invariant; hence at the
break point it is possible to predict the behaviour of large networks
by studying a much smaller network. The results also suggest that
if the tra c on a network increases exponentially, then there is a
maximum size that a network can have, after that the network will
always fail catastrophically.
To verify if catastrophic networks built using our algorithmic approach
can re
ect real situations, we evaluated the performance of a small
catastrophic network. By building the scenario using open source
network simulation software OMNet++, we were able to simulate a
router network using the Open Shortest Path First routing protocol
and carrying User Datagram Protocol tra c. Our results show that
this kind of networks can collapse as a cascade of failures. Furthermore,
recently the failure of Google Mail routers [1] con rms this kind
of catastrophic failure does occur in real situations
Cascading failures in spatially-embedded random networks
Cascading failures constitute an important vulnerability of interconnected
systems. Here we focus on the study of such failures on networks in which the
connectivity of nodes is constrained by geographical distance. Specifically, we
use random geometric graphs as representative examples of such spatial
networks, and study the properties of cascading failures on them in the
presence of distributed flow. The key finding of this study is that the process
of cascading failures is non-self-averaging on spatial networks, and thus,
aggregate inferences made from analyzing an ensemble of such networks lead to
incorrect conclusions when applied to a single network, no matter how large the
network is. We demonstrate that this lack of self-averaging disappears with the
introduction of a small fraction of long-range links into the network. We
simulate the well studied preemptive node removal strategy for cascade
mitigation and show that it is largely ineffective in the case of spatial
networks. We introduce an altruistic strategy designed to limit the loss of
network nodes in the event of a cascade triggering failure and show that it
performs better than the preemptive strategy. Finally, we consider a real-world
spatial network viz. a European power transmission network and validate that
our findings from the study of random geometric graphs are also borne out by
simulations of cascading failures on the empirical network.Comment: 13 pages, 15 figure
Networking - A Statistical Physics Perspective
Efficient networking has a substantial economic and societal impact in a
broad range of areas including transportation systems, wired and wireless
communications and a range of Internet applications. As transportation and
communication networks become increasingly more complex, the ever increasing
demand for congestion control, higher traffic capacity, quality of service,
robustness and reduced energy consumption require new tools and methods to meet
these conflicting requirements. The new methodology should serve for gaining
better understanding of the properties of networking systems at the macroscopic
level, as well as for the development of new principled optimization and
management algorithms at the microscopic level. Methods of statistical physics
seem best placed to provide new approaches as they have been developed
specifically to deal with non-linear large scale systems. This paper aims at
presenting an overview of tools and methods that have been developed within the
statistical physics community and that can be readily applied to address the
emerging problems in networking. These include diffusion processes, methods
from disordered systems and polymer physics, probabilistic inference, which
have direct relevance to network routing, file and frequency distribution, the
exploration of network structures and vulnerability, and various other
practical networking applications.Comment: (Review article) 71 pages, 14 figure
Cascading attacks in Wi-Fi networks: demonstration and counter-measures
Wi-Fi (IEEE 802.11) is currently one of the primary media to access the Internet. Guaranteeing the availability of Wi-Fi networks is essential to numerous online activities, such as e-commerce, video streaming, and IoT services. Attacks on availability are generally referred to as Denial-of-Service (DoS) attacks. While there exists signif- icant literature on DoS attacks against Wi-Fi networks, most of the existing attacks are localized in nature, i.e., the attacker must be in the vicinity of the victim. The purpose of this dissertation is to investigate the feasibility of mounting global DoS attacks on Wi-Fi networks and develop effective counter-measures.
First, the dissertation unveils the existence of a vulnerability at the MAC layer of Wi-Fi, which allows an adversary to remotely launch a Denial-of-Service (DoS) attack that propagates both in time and space. This vulnerability stems from a coupling effect induced by hidden nodes. Cascading DoS attacks can congest an entire network and do not require the adversary to violate any protocol. The dissertation demonstrates the feasibility of such attacks through experiments with real Wi-Fi cards, extensive ns-3 simulations, and theoretical analysis. The simulations show the attack is effective both in networks operating under fixed and varying bit rates, as well as ad hoc and infrastructure modes. To gain insight into the root-causes of the attack, the network is modeled as a dynamical system and its limiting behavior is analyzed. The model predicts that a phase transition (and hence a cascading attack) is possible when the retry limit parameter of Wi-Fi is greater or equal to 7.
Next, the dissertation identifies a vulnerability at the physical layer of Wi-Fi that allows an adversary to launch cascading attacks with weak interferers. This vulnerability is induced by the state machine’s logic used for processing incoming packets. In contrast to the previous attack, this attack is effective even when interference caused by hidden nodes do not corrupt every packet transmission. The attack forces Wi-Fi rate adaptation algorithms to operate at a low bit rate and significantly degrades network performance, such as communication reliability and throughput.
Finally, the dissertation proposes, analyzes, and simulates a method to prevent such attacks from occurring. The key idea is to optimize the duration of packet transmissions. To achieve this goal, it is essential to properly model the impact of MAC overhead, and in particular MAC timing parameters. A new theoretical model is thus proposed, which relates the utilization of neighboring pairs of nodes using a sequence of iterative equations and uses fixed point techniques to study the limiting behavior of the sequence. The analysis shows how to optimally set the packet duration so that, on the one hand, cascading DoS attacks are avoided and, on the other hand, throughput is maximized. The analytical results are validated by extensive ns-3 simulations. A key insight obtained from the analysis and simulations is that IEEE 802.11 networks with relatively large MAC overhead are less susceptible to cascading DoS attacks than networks with smaller MAC overhead
The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena
The Internet is the most complex system ever created in human history.
Therefore, its dynamics and traffic unsurprisingly take on a rich variety of
complex dynamics, self-organization, and other phenomena that have been
researched for years. This paper is a review of the complex dynamics of
Internet traffic. Departing from normal treatises, we will take a view from
both the network engineering and physics perspectives showing the strengths and
weaknesses as well as insights of both. In addition, many less covered
phenomena such as traffic oscillations, large-scale effects of worm traffic,
and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex
System
- …