Structural Controllability of Networks for Non-interactive Adversarial Vertex Removal

Alcaraz, Cristina; Miciolino, Estefanía Etchevés; Wolthusen, Stephen D.

doi:10.1007/978-3-319-03964-0_11

Cited by 15 publications

(26 citation statements)

References 20 publications

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“…Lin [15] defines G(A, B) = (V, E) as a digraph where V = V A ∪ V B is the set of vertices and E = E A ∪ E B is the set of edges. In this representation, V B comprises the nodes capable of injecting control signals into the entire network, also known as driver nodes (denoted as n d ) corresponding to input vector u in Equation (1). The identification of these nodes has so far been studied in relation to general networks.…”

Section: Introductionmentioning

confidence: 99%

“…This paper concentrates on power-law networks, most pertinent to a number of large-scale infrastructure networks. To identify the minimum driver node subsets N D , we follow the approach based on the power dominating set (PDS) problem, which is described in more detail in [1,2]. This interest is primarily because PDS-based networks have similar logical structures as real-world monitoring systems, where driver nodes can represent, for example, remote terminal units that control industrial sensors and actuators.…”

Section: Introductionmentioning

confidence: 99%

“…Building on previous work [1,2], this paper proposes several restoration strategies for controlling a network after N D has been perturbed. Different attack patterns that compromise nodes and the effects of the attacks have been considered extensively in [1,2], in particular, the analysis and evaluation of interactive and non-interactive attacks, including multiple rounds between attackers and defenders, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recovery of Structural Controllability for Control Systems

Alcaraz

Wolthusen

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Fundamental problems in control systems theory are controllability and observability, and designing control systems so that these properties are satisfied or approximated sufficiently. However, it is prudent to assume that an attacker will not only be able to subvert measurements but also control the system. Moreover, an advanced adversary with an understanding of the control system may seek to take over control of the entire system or parts thereof, or deny the legitimate operator this capability. The effectiveness of such attacks has been demonstrated in previous work. Indeed, these attacks cannot be ruled out given the likely existence of unknown vulnerabilities, increasing connectivity of nominally air-gapped systems and supply chain issues. The ability to rapidly recover control after an attack has been initiated and to detect an adversary's presence is, therefore, critical. This paper focuses on the problem of structural controllability, which has recently attracted substantial attention through the equivalent problem of the power dominating set introduced in the context of electrical power network control. However, these problems are known to be N P-hard with poor approximability. Given their relevance to many networks, especially power networks, this paper studies strategies for the efficient restoration of controllability following attacks and attacker-defender interactions in power-law networks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recovery of Structural Controllability for Control Systems

Alcaraz

Wolthusen

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…An interesting approach is developed in [32], [33] to restore/maintain controllability of networks in the presence of adversarial attacks or failure that remove some nodes/links. The authors apply a novel computationally efficient sub-optimal approximation for the restoration of the Power Dominating Set (PDS) 1 .…”

mentioning

confidence: 99%

“…The authors apply a novel computationally efficient sub-optimal approximation for the restoration of the Power Dominating Set (PDS) 1 . The authors apply their restoration strategy over different random models including Erdos-Renyi (ER), Small-World (SW), and Scale-Free (SF) graphs [32] and also provide a thorough complexity analysis of their method [33]. Further, this restoration strategy is applied for cloud-based monitoring of Cyber-Physical-Systems (CPS) [35], where a local attack detection technique based on the opinion dynamics 1 A set SG is a Dominating Set of the graph G = {V, E } if every node in V \ SG has a neighbor node in SG.…”

mentioning

confidence: 99%

Minimal Driver Nodes for Structural Controllability of Large-Scale Dynamical Systems: Node Classification

Doostmohammadian

2020

IEEE Systems Journal

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This paper considers the problem of minimal control inputs to affect the system states such that the resulting system is structurally controllable. This problem and the dual problem of minimal observability are claimed to have no polynomial-order exact solution and, therefore, are NP-hard. Here, adopting a graph-theoretic approach, this problem is solved for general nonlinear (and also structure-invariant) systems and a P-order solution is proposed. In this direction, the dynamical system is modeled as a directed graph, called system digraph, and two types of graph components are introduced which are tightly related with structural controllability. Two types of nodes which are required to be affected (or driven) by an input, called driver nodes, are defined, and minimal number of these driver nodes are obtained. Polynomial-order complexity of the given algorithms to solve the problem ensures applicability of the solution for analysis of large-scale dynamical systems. The structural results in this paper are significant as compared to the existing literature which offer approximate and computationally lessefficient, e.g. Gramian-based, solutions for the problem, while this paper provides exact solution with lower computational complexity and applicable for controllability analysis of nonlinear systems.

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