1,051 research outputs found
A Microscopic-view Infection Model based on Linear Systems
Understanding the behavior of an infection network is typically addressed from either a microscopic or a macroscopic point-of-view. The trade-off is between following the individual states at some added complexity cost or looking at the ratio of infected nodes. In this paper, we focus on developing an alternative approach based on dynamical linear systems that combines the fine information of the microscopic view without the associated added complexity. Attention is shifted towards the problems of source localization and network topology discovery in the context of infection networks where a subset of the nodes is elected as observers. Finally, the possibility to control such networks is also investigated. Simulations illustrate the conclusions of the paper with particular interest on the relationship of the aforementioned problems with the topology of the network and the selected observer/controller nodes
Fault Detection for Cyber-Physical Systems: Smart Grid case
The problem of fault detection and isolation in
cyber-physical systems is growing in importance following the
trend to have an ubiquitous presence of sensors and actuators
with network capabilities in power networks and other areas. In
this context, attacks to power systems or other vital components
providing basic needs might either present a serious threat or
at least cost a lot of resources. In this paper, we tackle the
problem of having an intruder corrupting a smart grid in two
different scenarios: a centralized detector for a portion of the
network and a fully distributed solution that only has limited
neighbor information. For both cases, differences in strategies
using Set-Valued Observers are discussed and theoretical results
regarding a bound on the maximum magnitude of the attacker’s
signal are provided. Performance is assessed through simulation,
illustrating, in particular, the detection time for various
types of faults in IEEE testbed scenarios
A general discrete-time method to achieve resilience in consensus algorithms
In this paper, we approach the problem of a set
of network agents reaching resilient consensus in the presence of a subset of attacked nodes. We devise a generalized
method, with polynomial time complexity, which receives as
input a discrete-time, synchronous-communication consensus
algorithm, a dynamic network of agents, and the maximum
number of attacked nodes. The distributed algorithm enables
each normal node to detect and discard the values of the
attacked agents while reaching the consensus of normal agents
for the input consensus algorithm. Hence, the proposed method
adds an extra layer of resilience to a given discrete-time and
synchronous-communication consensus algorithm. Finally, we
demonstrate the effectiveness of the method with experimental
results, showing some attack circumstances which we can
counter, where the state-of-the-art methods fail
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