Detecting link failures in complex network processes using remote monitoring

Dhal, Rahul; Torres, Jackeline Abad; Roy, Sandip

doi:10.1016/j.physa.2015.04.024

Cited by 28 publications

(16 citation statements)

References 28 publications

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“…For a predetermined set of input nodes K, the focus of our analysis is to characterize the performance of the detector (5), in terms of the network's adjacency matrix G. The performance of the detector ( 5) is measured by its error probability, which is given by…”

Section: Preliminaries and Problem Setupmentioning

confidence: 99%

“…In recent years, researchers have proposed a number of model-based and heuristic approaches for detecting and mitigating attacks against the actuators and the sensors in the network (see [2] and the references therein). Despite the success of these studies in revealing the performance and the limitations of attack detection mechanisms, several challenges remain, particularly in distinguishing malicious signals from ambient data, selecting optimal sensor locations to maximize the detection performance [3,4], and deriving simple graphical rubrics to readily evaluate and optimize network security [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Network Theoretic Analysis of Maximum a Posteriori Detectors for Optimal Input Detection

Anguluri¹,

Katewa²,

Roy³

et al. 2020

Preprint

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In this paper we characterize the performance of a class of maximum-a-posteriori (MAP) detectors for network systems driven by unknown stochastic inputs, as a function of the location of the sensors and the topology of the network. We consider two scenarios: one in which the changes occurs in the mean of the input, and the other where the changes are allowed to happen in the covariance (or power) of the input. In both the scenarios, to detect the changes, we associate suitable MAP detectors for a given set of sensors, and study its detection performance as function of the network topology, and the graphical distance between the input nodes and the sensors location. When the input and measurement noise follow a Gaussian distribution, we show that, as the number of measurements goes to infinity, the detectors' performance can be studied using the input to output gain of the transfer function of the network system. Using this characterization, we derive conditions under which the detection performance obtained when the sensors are located on a network cut is not worse (resp. not better) than the performance obtained by measuring all nodes of the subnetwork induced by the cut and not containing the input nodes. Our results provide structural insights into the sensor placement from a detection-theoretic viewpoint. Finally, we illustrate our findings via numerical examples.

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Section: Preliminaries and Problem Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network Theoretic Analysis of Maximum a Posteriori Detectors for Optimal Input Detection

Anguluri¹,

Katewa²,

Roy³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…42 In addition, the expressions indicate that manipulation is difficult, if the entries in the eigenvectors of A (and hence of L) corresponding to the measurement location are small. Recently, graph-theoretic results on the eigenvector components of the Laplacian have also been established, see, eg, the works of Dhal et al 43 and Torres and Roy. 44 These results indicate that the slow modal dynamics can be most easily manipulated from extreme points in the graph, but overall controllability is often strongest near the center; details are omitted.…”

Section: Theorem 3 the Trace Of The Inverse Of Reachabilitymentioning

confidence: 99%

Local open‐ and closed‐loop manipulation of multiagent networks

Sahabandu

Torres

Dhal³

et al. 2018

Intl J Robust & Nonlinear

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The manipulation of networked cyberphysical devices via local external actuation or feedback control is explored, in the context of a canonical multiagent dynamical system that is engaged in a consensus or synchronization task. One main focus is to understand whether or not, and how easily, a stakeholder can manipulate the full network's dynamics by hijacking only one agent's actuation signal. Explicit spectral characterizations are given of the energy (effort) required to manipulate the dynamics. These characterizations are used to (1) gain structural insights into ease of manipulation, (2) show that manipulation along the consensus manifold is easy, and (3) address network design to enable or prevent manipulation. Additionally, it is shown that the multiagent system can be manipulated effectively along the consensus manifold using local feedback controls, which do not require model knowledge or wide-area measurements.SAHABANDU ET AL. topology among the network's components. Thus, local actions in a CPN incur patterned wide-area impacts, regardless of the specifics of the local action or the network dynamics. In this work, we seek to understand whether and how local actuations can be designed to manipulate the network's wide-area behavior and to evaluate extreme impacts (whether beneficial or harmful). To do this, we consider a linear double-integrator network (DIN) model, which serves as a common abstraction for a number of CPN processes defined on a graph (eg, vehicle traffic on a highway, formation-controlled aerial vehicle teams, linearized swing dynamics of the electric power grid, and certain sensor-network algorithms). [6][7][8] This article explores the manipulation of the DIN via external actuation or feedback control at a single agent. First, we study whether or not the actuation signal for an agent can be designed to move the network's state to an arbitrary value and also the actuation effort (energy) required for manipulation. Characterizations are obtained in terms of the network's coupling parameters and the location of the manipulated agent. As a comparison, manipulation via a local feedback control is also considered, wherein only local measurements are used to decide the additional actuation of the agent. This is done by analyzing the transfer function of the dynamics from the perspective of the control channel, which then indicates whether or not the system can be manipulated to achieve certain control objectives (eg, fast tracking or disturbance rejection) in a feedback setting.The research described here contributes to an important research thrust on the security of cyberphysical systems (CPSs), which is a key property required for their effective operation (eg, the work of Banerjee et al 9 ). CPS security encompasses security of information, ie, the protection of sensitive or private information that is held by the CPS. In complement, CPS security also encompasses functional security, ie, protection of CPS functions from attacks and failures. The need for functional security for CPS is pa...

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“…Recently, graph-theoretic results on the eigenvector components of the Laplacian have also been established, see e.g. [21,22]. These results indicate the slow modal dynamics can be most easily manipulated from extreme points in the graph, but overall controllability is often strongest near the center; details are omitted.…”

Section: Formula For the Gramian Inversementioning

confidence: 99%

Local open- and closed-loop manipulation of multi-agent networks

Torres

Sahabandu

Dhal

et al. 2015

Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems

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We explore the manipulation of networked cyber-physical devices via external actuation or feedback control at a single location, in the context of a canonical multi-agent system model known as the double integrator network. One main focus is to understand whether or not, and how easily, a stakeholder can manipulate network's full dynamics by designing the actuation signal for one agent (in an open-loop sense). Additionally, we investigate the ability of the stakeholder to manipulate the multi-agent system, and achieve control objectives, via local feedback control. For both problems, we find that manipulation of the dynamics is crucially dependent on the network's graph and associated spectrum.

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Detecting link failures in complex network processes using remote monitoring

Cited by 28 publications

References 28 publications

Network Theoretic Analysis of Maximum a Posteriori Detectors for Optimal Input Detection

Network Theoretic Analysis of Maximum a Posteriori Detectors for Optimal Input Detection

Local open‐ and closed‐loop manipulation of multiagent networks

Local open- and closed-loop manipulation of multi-agent networks

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