196,039 research outputs found
Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependency
Increased coupling between critical infrastructure networks, such as power
and communication systems, will have important implications for the reliability
and security of these systems. To understand the effects of power-communication
coupling, several have studied interdependent network models and reported that
increased coupling can increase system vulnerability. However, these results
come from models that have substantially different mechanisms of cascading,
relative to those found in actual power and communication networks. This paper
reports on two sets of experiments that compare the network vulnerability
implications resulting from simple topological models and models that more
accurately capture the dynamics of cascading in power systems. First, we
compare a simple model of topological contagion to a model of cascading in
power systems and find that the power grid shows a much higher level of
vulnerability, relative to the contagion model. Second, we compare a model of
topological cascades in coupled networks to three different physics-based
models of power grids coupled to communication networks. Again, the more
accurate models suggest very different conclusions. In all but the most extreme
case, the physics-based power grid models indicate that increased
power-communication coupling decreases vulnerability. This is opposite from
what one would conclude from the coupled topological model, in which zero
coupling is optimal. Finally, an extreme case in which communication failures
immediately cause grid failures, suggests that if systems are poorly designed,
increased coupling can be harmful. Together these results suggest design
strategies for reducing the risk of cascades in interdependent infrastructure
systems
Information Leakage Games
We consider a game-theoretic setting to model the interplay between attacker
and defender in the context of information flow, and to reason about their
optimal strategies. In contrast with standard game theory, in our games the
utility of a mixed strategy is a convex function of the distribution on the
defender's pure actions, rather than the expected value of their utilities.
Nevertheless, the important properties of game theory, notably the existence of
a Nash equilibrium, still hold for our (zero-sum) leakage games, and we provide
algorithms to compute the corresponding optimal strategies. As typical in
(simultaneous) game theory, the optimal strategy is usually mixed, i.e.,
probabilistic, for both the attacker and the defender. From the point of view
of information flow, this was to be expected in the case of the defender, since
it is well known that randomization at the level of the system design may help
to reduce information leaks. Regarding the attacker, however, this seems the
first work (w.r.t. the literature in information flow) proving formally that in
certain cases the optimal attack strategy is necessarily probabilistic
Node Removal Vulnerability of the Largest Component of a Network
The connectivity structure of a network can be very sensitive to removal of
certain nodes in the network. In this paper, we study the sensitivity of the
largest component size to node removals. We prove that minimizing the largest
component size is equivalent to solving a matrix one-norm minimization problem
whose column vectors are orthogonal and sparse and they form a basis of the
null space of the associated graph Laplacian matrix. A greedy node removal
algorithm is then proposed based on the matrix one-norm minimization. In
comparison with other node centralities such as node degree and betweenness,
experimental results on US power grid dataset validate the effectiveness of the
proposed approach in terms of reduction of the largest component size with
relatively few node removals.Comment: Published in IEEE GlobalSIP 201
Vulnerability and Protection of Critical Infrastructures
Critical infrastructure networks are a key ingredient of modern society. We
discuss a general method to spot the critical components of a critical
infrastructure network, i.e. the nodes and the links fundamental to the perfect
functioning of the network. Such nodes, and not the most connected ones, are
the targets to protect from terrorist attacks. The method, used as an
improvement analysis, can also help to better shape a planned expansion of the
network.Comment: 4 pages, 1 figure, 3 table
Active Virtual Network Management Prediction: Complexity as a Framework for Prediction, Optimization, and Assurance
Research into active networking has provided the incentive to re-visit what
has traditionally been classified as distinct properties and characteristics of
information transfer such as protocol versus service; at a more fundamental
level this paper considers the blending of computation and communication by
means of complexity. The specific service examined in this paper is network
self-prediction enabled by Active Virtual Network Management Prediction.
Computation/communication is analyzed via Kolmogorov Complexity. The result is
a mechanism to understand and improve the performance of active networking and
Active Virtual Network Management Prediction in particular. The Active Virtual
Network Management Prediction mechanism allows information, in various states
of algorithmic and static form, to be transported in the service of prediction
for network management. The results are generally applicable to algorithmic
transmission of information. Kolmogorov Complexity is used and experimentally
validated as a theory describing the relationship among algorithmic
compression, complexity, and prediction accuracy within an active network.
Finally, the paper concludes with a complexity-based framework for Information
Assurance that attempts to take a holistic view of vulnerability analysis
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