Data-Based Tampered-Data Recovery Strategy With Encoding Against Stealthy Attack for Unknown Discrete-Time Systems.

This study addresses a tampered-data recovery problem for linear discrete-time systems with completely unknown system dynamics under stealthy attacks. The basic idea is to identify the stealthy attack, that lies in any of attack-stealthy subspaces, and compensate for it. Different from the existing sparse recovery methods which are applicable to nonstealthy sparse attacks, a novel encoding scheme, where a set of subdecoding matrices is designed specifically for each 1-D attack-stealthy subspace, is developed so that the parameters of the stealthy attack can be identified via a subspace projection technique.

Event-Triggered Distributed Hypothesis Testing for Multiagent Networks Based on Observations Cumulation.

This article is concerned with the distributed hypothesis testing problem for multiagent networks, where a group of agents aim to learn an optimal hypothesis set via informative observations and event-triggered communication. Within this framework, a new event-triggered distributed hypothesis testing algorithm based on cumulation of historical observations is proposed. Theoretically, it is proven that due to the introduction of cumulation of historical observations, the proposed algorithm can always ensure the convergence whatever the event-triggered parameters are selected.

Distributed State Estimation for Linear Systems in Networks With Antagonistic Interactions.

This article deals with the distributed state estimation problem for linear systems in networks with cooperative interactions and antagonistic interactions, where the cooperative interactions and the antagonistic interactions are characterized by the positive weights and the negative weights, respectively. Due to the coexistence of the cooperative interactions and the antagonistic interactions, the existing methods based on the non-negatively weighted graph become not applicable. First, a partition method of the nodes and a decomposition form of the system matrices are introduced.

Optimal Stealthy Attacks With Energy Constraint Against Remote State Estimation.

This article investigates the problem of energy-constrained stealthy attack strategy against remote state estimation for cyber-physical systems. Taking into account the energy constraint, the malicious attacker is required to schedule the off-line generated signals to modify the transmitted data with limited times over a finite-time horizon under the stealthiness condition, which makes the design of attack strategy more complex. Different from the attack schedules which are studied on the basis of prescribed attack signals in the existing results, the attack strategy is presented under the framework of collaborative design to deteriorate the estimation performance to the largest extent, which yet leads to the coupling between the attack schedules and attack signals.

Fuzzy Adaptive Finite-Time Resilient Control Against Unknown False Data Injection Attacks for MIMO Nonlinear Switched Systems With Unknown Dead Zone.

This article studies the finite-time adaptive resilient control problem for MIMO nonlinear switched systems with the unknown dead zone. The sensors of the controlled systems suffer from unknown false data injection (FDI) attacks so that all states cannot be directly applied to the design process of the controller. To address this negative impact of FDI attacks, a new coordinate transformation is designed in control design.

Clinical efficacy of tetrandrine in artificial stone-associated silicosis: A retrospective cohort study.

Background: Outbreaks of silicosis have occurred among workers in the artificial stone (AS) industry, and there is currently no effective antifibrosis treatment for silicosis.

Design: A retrospective cohort study.

Methods: We retrospectively analyzed the clinical data of 89 artificial stone-associated silicosis patients treated in Shanghai Pulmonary Hospital (China).

Optimal energy constrained deception attacks in cyber-physical systems with multiple channels: A fusion attack approach.

This paper studies the issue of developing the optimal deception attacks on the multiple channels in cyber-physical systems, where the attackers are limited by energy constraints. To fully utilize the eavesdropped data, by linearly combining the innovations from the different channels, a fusion attack model is proposed under the stealthiness condition. According to the statistical characteristics of the correlated stochastic variables and the orthogonality principle, the state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrices of the remote estimators under the proposed attack framework.

Distributed Event-Triggered Algorithm Designs for Resource Allocation Problems via a Universal Scalar Function-Based Analysis.

In this article, we are concerned with distributed algorithm designs for resource allocation problems via event-triggered communication. The target is to search an optimal resource allocation scheme such that the summation of objective functions is minimized. Due to communication efficiency and privacy concerns, distributed algorithms with event-triggered communications are proposed in this article.

Event-Based Remote State Estimation for Nonlinear Systems: A Box Particle Filtering Method.

This article is concerned with the problem of event-based remote state estimation for nonlinear/non-Gaussian systems on a wireless network with limited bandwidth. To reduce unnecessary data transmissions, a novel event-triggering mechanism is developed by using the least-square technique. Based on this, an event-triggered box particle filtering scheme is designed to realize the minimum mean-squared error estimation at the remote estimator end, in which the posterior probability density functions are calculated separately according to the information of the event-triggered indicator to avoid the problem of excessive estimation error.

Secure State Estimation of Nonlinear Cyber-Physical Systems Against DoS Attacks: A Multiobserver Approach.

This article focuses on the problem of secure state estimation for cyber-physical systems (CPSs), whose physical plants are modeled as nonlinear strict-feedback systems. The measured output is sent to the designed observer over a wireless communication network subject to denial-of-service (DoS) attacks. Due to the energy constraints of the attackers, the attack duration is upper bounded.

Distributed Fuzzy Adaptive Output-Feedback Control of Unknown Nonlinear Multiagent Systems in Strict-Feedback Form.

This article is concerned with the cooperative tracking control problem for heterogeneous multiagent systems in a leader-following form under a directed graph. The dynamics of each following agent is unknown, obeying a strict-feedback form. With the help of fuzzy-logic systems, input filters, and constraint-handling schemes, a fully distributed output-feedback control algorithm is proposed to achieve output synchronization with prescribed performance and guarantee boundedness of signals in the closed-loop systems.

Kullback-Leibler Divergence-Based Optimal Stealthy Sensor Attack Against Networked Linear Quadratic Gaussian Systems.

This article concentrates on designing optimal stealthy attack strategies for cyber-physical systems (CPSs) modeled by the linear quadratic Gaussian (LQG) dynamics, where the attacker aims to increase the quadratic cost maximally and keeping a certain level of stealthiness by simultaneously intercepting and modifying the transmitted measurements. In our work, a novel attack model is developed, based on which the attacker can launch strictly stealthy or ϵ -stealthy attacks. To remain strictly stealthy, the attacker only needs to solve an off-line semidefinite program problem.

Secure State Estimation With Switched Compensation Mechanism Against DoS Attacks.

This article is concerned with the secure state estimation problem for cyber-physical systems under intermittent denial-of-service (DoS) attacks. Based on a switching scheme and the cascade observer technique, a novel resilient state observer with a switched compensation mechanism is designed. Moreover, a quantitative relationship between the resilience against DoS attacks and the design parameters is revealed.

Remote State Estimation for Nonlinear Systems via a Fading Channel: A Risk-Sensitive Approach.

This article investigates the problem of remote state estimation for nonlinear systems via a fading channel, where the packet losses may occur over the sensor-to-estimator communication network. The risk-sensitive (RS) approach is introduced to formulate the estimation problem with intermittent measurements such that an exponential cost criterion is minimized. Based on the reference measure method, the closed-form expression of the nonlinear RS estimator is derived.

Resilient Event-Triggered Distributed State Estimation for Nonlinear Systems Against DoS Attacks.

This article investigates the resilient event-triggered (ET) distributed state estimation problem for nonlinear systems under denial-of-service (DoS) attacks. Different from the existing results mainly considering linear or specified nonlinear systems, more general nonlinear systems are considered in this study. Moreover, the considered DoS attacks are able to compromise different communication links among estimators independently.

Optimal Steady-State Regulator Design for a Class of Nonlinear Systems With Arbitrary Relative Degree.

In this article, we consider the regulator design problem for a class of uncertain multi-input-multioutput (MIMO) nonlinear systems with arbitrary relative degree. The objective is to regulate the output of the nonlinear system to an optimal steady state that solves a constrained optimization problem, without computing the optimal solution in advance. By embedding saddle-point dynamics, both state and output-feedback-based regulators are proposed and the resulting closed-loop systems are modeled in standard singularly perturbed forms.

Event-Triggered Distributed State Estimation for Multiagent Systems Under DoS Attacks.

This article investigates the problem of event-triggered distributed state estimation for linear multiagent systems under denial-of-service (DoS) attacks. In contrast to the previous studies where the agents have access to the measurements of their own states, an estimation algorithm is provided based on the measurements relative to the adjacent agents, and the considered DoS attacks jam each channel independently while the attack durations are constrained. To estimate the states and effectively schedule the information transmissions over the network possibly subject to malicious attacks, a prediction-based switching observer scheme with an event-triggered communication strategy is proposed, and the invalidation problem of the event-triggering mechanism caused by the DoS attacks is solved.

Event-Triggered Distributed State Estimation for Cyber-Physical Systems Under DoS Attacks.

This article investigates the event-triggered distributed state estimation problem for a class of cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks. First, an observer-based event-triggered transmission scheme is proposed to improve the transmission efficiency, and the corresponding distributed Kalman filter is designed to estimate the system states. Under the collective observability condition, a relationship between estimation error covariance, attack intensity, and transmission efficiency is established by utilizing the covariance intersection fusion method and the property of matrix congruent transformation rank.

Histone demethylase JHDM2A regulates H3K9 dimethylation in response to arsenic-induced DNA damage and repair in normal human liver cells.

Long-term arsenic exposure is a worldwide public health problem that causes serious harm to human health. The liver is the main target organ of arsenic toxicity; arsenic induces disruption of the DNA damage repair pathway, but its mechanisms remain unclear. In recent years, studies have found that epigenetic mechanisms play an important role in arsenic-induced lesions.

Supervisory Nonlinear State Observers for Adversarial Sparse Attacks.

This article investigates the problem of secure state estimation for continuous-time linear systems in the presence of sparse sensor attacks. Compared with the existing results, the attacked sensor set can be changed by adversaries against secure estimation. To address the more erratic attacks, a novel supervisory state observer is proposed, which employs a bank of candidate nonlinear subobservers and a switching logic administrated by a monitoring function to select the active subobserver at every instant of time.

Stability Analysis for Cyber-Physical Systems Under Denial-of-Service Attacks.

This article investigates the stability analysis problem for cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. Based on the real-time data characterizing the suffered DoS attack, a necessary and sufficient condition for the closed-loop stability in the presence of DoS attacks is provided. Besides, by transforming stability analysis the system under DoS into stability analysis of an auxiliary system, novel sufficient conditions for the closed-loop stability, which can be verified more easily than the necessary and sufficient condition, are provided.

Neural Learning-Based Fixed-Time Consensus Tracking Control for Nonlinear Multiagent Systems With Directed Communication Networks.

This article investigates the problem of fixed-time consensus tracking for nonlinear multiagent systems. Different from the existing studies where the follower systems are linear or pure integrator-type systems, in this article, the follower systems have completely unknown nonlinear functions and time-varying disturbances. Within this framework, a fixed-time observer-based distributed control strategy is proposed to realize the consensus tracking.

Optimal Stealth Attack Strategy Design for Linear Cyber-Physical Systems.

This article studies the problem of the optimal stealth attack strategy design for linear cyber-physical systems (CPSs). Virtual systems that reflect the attacker's target are constructed, and a linear attack model with varying gains is designed based on the virtual models. Unlike the existing optimal stealth attack strategies that are designed based on sufficient conditions, necessary and sufficient conditions are, respectively, established to achieve the optimal attack performance while maintaining stealth in virtue of the solvability of certain coupled recursive Riccati difference equations (RDEs).

Event-Triggered H Control for T-S Fuzzy Systems via New Asynchronous Premise Reconstruction Approach.

This article studies the problem of H controller design for discrete-time T-S fuzzy systems under an event-triggered (ET) communication mechanism. By proposing a new asynchronous premise reconstruction approach, new types of ET fuzzy controllers are designed to overcome the challenges caused by the mismatch of premise variables, in which the gains of the designed controllers are automatically updated at different triggering instants according to an online algorithm. By constructing discontinuous Lyapunov functions, it is proved that the proposed ET controllers guarantee the stability and H performance of the closed-loop systems.