8 research outputs found
Finite-time Average Consensus in a Byzantine Environment Using Stochastic Set-Valued Observers
Compute the average of the initial states in finite-time. Guarantee fault detection and bounds on the maximum
possible deviation from an attack. Incorporate the transmissions stochastic information in the
fault detection mechanism
Distributed Fault Detection Using Relative Information in Linear Multi-Agent Networks
This paper addresses the problem of fault detection in the context of a collection of agents performing a shared task and exchanging relative information over a communication network. We resort to techniques in the literature to construct a meaningful observable system and overcome the issue that the system of systems is not observable. A solution involving Set-Valued Observers (SVOs) is proposed to estimate the state in a distributed fashion and a proof of convergence of the estimates is given under mild assumptions. The performance of the proposed algorithm is assessed through simulations
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
The Distributed Convergence Classifier Using the Finite Difference
The paper presents a novel distributed classifier of the convergence, which allows to detect the convergence/the divergence of a distributed converging algorithm. Since this classifier is supposed to be primarily applied in wireless sensor networks, its proposal makes provision for the character of these networks. The classifier is based on the mechanism of comparison of the forward finite differences from two consequent iterations. The convergence/the divergence is classifiable only in terms of the changes of the inner states of a particular node and therefore, no message redundancy is required for its proper functionality
Gossip Average Consensus in a Byzantine Environment Using Stochastic Set-Valued Observers
Abstract — We address the problem of a consensus system in the presence of Byzantine faults seen as an attacker injecting a perturbation in the state of the nodes. We propose the use of Set-Valued Observers to detect if the state observations are compatible with the system dynamics. The method is extended to the stochastic case by introducing a strategy to construct a set that is guaranteed to contain all possible states with, at least, a pre-specified desired probability. The proposed algorithm is stable in the sense that it requires a finite number of vertices to represent polytopic sets while also enabling the a priori computation of the largest magnitude of a disturbance that an attacker can inject without being detected. I
Gossip Average Consensus in a Byzantine Environment Using Stochastic Set-Valued Observers
We address the problem of a consensus system in the presence of Byzantine faults seen as an attacker injecting a perturbation in the state of the nodes. We propose the use of Set-Valued Observers to detect if the state observations are compatible with the system dynamics. The method is extended to the stochastic case by introducing a strategy to construct a set that is guaranteed to contain all possible states with, at least, a pre-specified desired probability. The proposed algorithm is stable in the sense that it requires a finite number of vertices to represent polytopic sets while also enabling the a priori computation of the largest magnitude of a disturbance that an attacker can inject without being detected
Fault-tolerant Stochastic Distributed Systems
The present doctoral thesis discusses the design of fault-tolerant distributed systems, placing emphasis in addressing the case where the actions of the nodes or their interactions are stochastic. The main objective is to detect and identify faults to improve the resilience of distributed systems to crash-type faults, as well as detecting the presence of malicious nodes in pursuit of exploiting the network. The proposed analysis considers malicious agents and computational solutions to detect faults.
Crash-type faults, where the affected component ceases to perform its task, are tackled in this thesis by introducing stochastic decisions in deterministic distributed algorithms. Prime importance is placed on providing guarantees and rates of convergence for the steady-state solution. The scenarios of a social network (state-dependent example) and consensus (time- dependent example) are addressed, proving convergence. The proposed algorithms are capable of dealing with packet drops, delays, medium access competition, and, in particular, nodes failing and/or losing network connectivity.
The concept of Set-Valued Observers (SVOs) is used as a tool to detect faults in a worst-case scenario, i.e., when a malicious agent can select the most unfavorable sequence of communi- cations and inject a signal of arbitrary magnitude. For other types of faults, it is introduced the concept of Stochastic Set-Valued Observers (SSVOs) which produce a confidence set where the state is known to belong with at least a pre-specified probability. It is shown how, for an algorithm of consensus, it is possible to exploit the structure of the problem to reduce the computational complexity of the solution. The main result allows discarding interactions in the model that do not contribute to the produced estimates.
The main drawback of using classical SVOs for fault detection is their computational burden. By resorting to a left-coprime factorization for Linear Parameter-Varying (LPV) systems, it is shown how to reduce the computational complexity. By appropriately selecting the factorization, it is possible to consider detectable systems (i.e., unobservable systems where the unobservable component is stable). Such a result plays a key role in the domain of Cyber-Physical Systems (CPSs). These techniques are complemented with Event- and Self-triggered sampling strategies that enable fewer sensor updates. Moreover, the same triggering mechanisms can be used to make decisions of when to run the SVO routine or resort to over-approximations that temporarily compromise accuracy to gain in performance but maintaining the convergence characteristics of the set-valued estimates. A less stringent requirement for network resources that is vital to guarantee the applicability of SVO-based fault detection in the domain of Networked Control Systems (NCSs)