A Survey of Graph-Theoretic Approaches for Analyzing the Resilience of Networked Control Systems
As the scale of networked control systems increases and interactions between different subsystems become more sophisticated, questions of the resilience of such networks increase in importance. The need to redefine classical system and control-theoretic notions using the language of graphs has recently started to gain attention as a fertile and important area of research. This paper presents an overview of graph-theoretic methods for analyzing the resilience of networked control systems. We discuss various distributed algorithms operating on networked systems and investigate their resilience against adversarial actions by looking at the structural properties of their underlying networks. We present graph-theoretic methods to quantify the attack impact, and reinterpret some system-theoretic notions of robustness from a graph-theoretic standpoint to mitigate the impact of the attacks. Moreover, we discuss miscellaneous problems in the security of networked control systems which use graph-theory as a tool in their analyses. We conclude by introducing some avenues for further research in this field.
Code (0)
등록된 구현이 없습니다.
Tasks
MiscellaneousSimilar Papers 제목 키워드 기반
A Hetero-functional Graph Resilience Analysis for Convergent Systems-of-Systems
Our modern life has grown to depend on many and nearly ubiquitous large complex engineering systems. Many disciplines now seemingly ask the same question: ``In the face of assumed disruption, to what degree will these sy…
Language ModellingResilience in Knowledge Graph Embeddings
In recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation systems, amongst others. Large-scale know…
Graph EmbeddingInformation RetrievalKnowledge Graph EmbeddingKnowledge Graph Embeddings+3Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
Graph neural networks have become one of the most important techniques to solve machine learning problems on graph-structured data. Recent work on vertex classification proposed deep and distributed learning models to ac…
AllGeneral ClassificationGraph Neural NetworkResilience and Security of Deep Neural Networks Against Intentional and Unintentional Perturbations: Survey and Research Challenges
In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it is imperative that DNNs provide inference robust to external perturbations - both intentional and unintentional. Although the resilience of DNNs…
SurveyPhysics-Inspired Spatial Temporal Graph Neural Networks for Predicting Industrial Chain Resilience
Industrial chain plays an increasingly important role in the sustainable development of national economy. However, as a typical complex network, data-driven deep learning is still in its infancy in describing and analyzi…