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Cyber-physical systems (CPSs) have emerged as an essential area of research in the last decade, providing a new paradigm for the integration of computational and physical units in modern control systems. Remote state estimation (RSE) is an indispensable functional module of CPSs. Recently, it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE, leading to severe estimation performance degradation. This paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures, with a specific focus on integrity attacks against RSE. Firstly, two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed, which provide a deeper insight into the vulnerabilities of RSE. Secondly, a detailed review of typical attack detection and resilient estimation algorithms is included, illustrating the latest defensive measures safeguarding RSE from adversaries. Thirdly, some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers' and defenders' perspectives. Finally, several challenges and open problems are presented to inspire further exploration and future research in this field.

Safe and efficient operation of complex industrial processes is highly dependent on alarm systems. There is a phenomenon called alarm flood, which significantly impairs the performance of alarm systems. An alarm flood refers to a situation when a plant operator is overloaded with many alarms triggered sequentially within a short time period. As a result, the operator can be distracted from taking the required safety actions. It is, therefore, imperative to deal with the problem of alarm floods, particularly for online applications. By taking advantage of historical alarm and event (A&E) logs, efficient alarm flood monitoring methods based on similarity analysis can be developed in the area of machine learning (ML). In this article, we develop a probabilistic framework for transforming alarm flood data into suitable inputs for convolutional neural networks (CNNs). The proposed alarm flood representation is used to establish an ML-based alarm flood analysis framework, where a CNN is trained to predict upcoming fault scenarios by observing online alarm floods. This method is capable of incorporating the activated alarm tags and their triggering times, as well as tolerating the effect of irrelevant alarms and alarm order ambiguities. Two alarm datasets from the Tennessee Eastman (TE) benchmark and a Vinyl Acetate Monomer (VAM) process are used to evaluate the proposed approach.

This article studies the problem of false data injection (FDI) attacks against remote state estimation. The scenario that malicious attackers can intercept original data packets and also eavesdrop on some side information of system states with extra sensors is considered. To clarify a counterintuitive issue in existing work, a different innovation-based linear attack policy fusing all available information is proposed. First, the evolution of a posteriori estimation error covariance with FDI attacks is derived. Then, explicit solutions of optimal stealthy attack coefficients are obtained without solving optimization problems numerically. The condition under which there exist multiple optimal attacks is analyzed. In addition, an easy-to-check criterion for comparing two information fusion methods in scalar systems is given. Simulation results show that, compared with existing work, the proposed attack strategy can completely deceive the anomaly detector and cause more severe performance degradation in remote state estimation.

The development of single-cell RNA sequencing (scRNA-seq) technology has opened up a new perspective for us to study disease mechanisms at the single cell level. Cell clustering reveals the natural grouping of cells, which is a vital step in scRNA-seq data analysis. However, the high noise and dropout of single-cell data pose numerous challenges to cell clustering. In this study, we propose a novel matrix factorization method named NLRRC for single-cell type identification. NLRRC joins non-negative low-rank representation (LRR) and random walk graph regularized NMF (RWNMFC) to accurately reveal the natural grouping of cells. Specifically, we find the lowest rank representation of single-cell samples by non-negative LRR to reduce the difficulty of analyzing high-dimensional samples and capture the global information of the samples. Meanwhile, by using random walk graph regularization (RWGR) and NMF, RWNMFC captures manifold structure and cluster information before generating a cluster allocation matrix. The cluster assignment matrix contains cluster labels, which can be used directly to get the clustering results. The performance of NLRRC is validated on simulated and real single-cell datasets. The results of the experiments illustrate that NLRRC has a significant advantage in single-cell type identification.