In grid-connected inverter systems, frequency-domain passivity theory is increasingly employed to analyze grid-inverter interactions and guide inverter control designs. However, due to difficulties in meeting sufficient passivity-based stability conditions at low frequencies, passivity theory often falls short of achieving stable system specifications. This article introduces an extended frequency-domain passivity theory. By incorporating a weighting matrix, an extended stability condition is derived. Compared to conventional passivity-based stability conditions, the proposed theory significantly reduces conservativeness and is more suited for analyzing grid-inverter interactions and guiding inverter control design. Theoretical analyses, numerical examples, and experimental results are provided to validate the effectiveness of the proposed methods.

A resilient state estimation scheme for uniformly observable nonlinear systems, based on a method for local identification of sensor attacks, is presented. The estimation problem is combinatorial in nature, and so many methods require substantial computational and storage resources as the number of sensors increases. To reduce the complexity, the proposed method performs the attack identification with local subsets of the measurements, not with the set of all measurements. A condition for nonlinear attack identification is introduced as a relaxed version of existing redundant observability condition. It is shown that an attack identification can be performed even when the entire state cannot be recovered from the measurements. As a result, although a portion of measurements are compromised, they can be locally identified and excluded from the state estimation, and thus, the true state can be recovered. Simulation results demonstrate the effectiveness of the proposed scheme.

Eco-friendly freight operations are crucial for decarbonizing the transportation sector. Systematic analysis of policy measures requires a principled modeling approach. While the commonly used model referred to as routing game considers the congestible nature of transportation facilities, exiting models fail to account for environmental factors. This paper aims at providing a mathematical framework to study strategic interaction between owners of mixed fleets comprising of both internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. This study introduces a “green” routing game with incomplete information that models strategic interaction among multiple logistic operators. These players face a pollution tax imposed on ICEVs and a potential delayed delivery cost due to EV charging requirements with uncertainty. In contrast to existing models, this novel model captures the players' trade-off between lengthier congestion delay at charging stations as the share of EV trucks increases and higher pollution costs with increased ICEVs usage, with uncertainty determined by a latent state. We first provide equilibrium characterization and present a condition for essential uniqueness. We show that this equilibrium can be computed in a distributed manner using a gradient projection method. We then introduce a public information system that broadcasts real-time information about the latent state. Importantly, we analyze value of information for providing a condition for the public information to be beneficial. Finally, we present numerical examples to illustrate settings where environmental taxation and information dissemination can improve social welfare.

The paper deals with the setting where two viruses (say virus 1 and virus 2) coexist in a population, and they are not necessarily mutually exclusive, in the sense that infection due to one virus does not preclude the possibility of simultaneous infection due to the other. We develop a coupled bi-virus susceptible–infected–susceptible (SIS) model from a 4n-state Markov process, where n is the number of agents (i.e., individuals or subpopulation) in the population. We identify a sufficient condition for both viruses to eventually die out, and a sufficient condition for the existence, uniqueness and asymptotic stability of the endemic equilibrium of each virus. We establish a sufficient condition and multiple necessary conditions for local exponential convergence to the boundary equilibrium (i.e., one virus persists, the other one dies out) of each virus. Under mild assumptions on the healing rate, we show that there cannot exist a coexisting equilibrium where for each node there is a nonzero fraction infected only by virus 1; a nonzero fraction infected only by virus 2; but no fraction that is infected by both viruses 1 and 2. Likewise, assuming that healing rates are strictly positive, a coexisting equilibrium where for each node there is a nonzero fraction infected by both viruses 1 and 2, but no fraction is infected only by virus 1 (resp. virus 2) does not exist. Further, we provide a necessary condition for the existence of certain other kinds of coexisting equilibria. We show that, unlike the competitive bivirus model, the coupled bivirus model is not monotone. Finally, we illustrate our theoretical findings using an extensive set of simulations.

In this article, we establish regret lower bounds for adaptively controlling an unknown linear Gaussian system with quadratic costs. We combine ideas from experiment design, estimation theory, and a perturbation bound of certain information matrices to derive regret lower bounds exhibiting scaling on the order of magnitude root T in the time horizon T . Our bounds accurately capture the role of control-theoretic parameters and we are able to show that systems that are hard to control are also hard to learn to control; when instantiated to state feedback systems we recover the dimensional dependency of earlier work but with improved scaling with system-theoretic constants, such as system costs and Gramians. Furthermore, we extend our results to a class of partially observed systems and demonstrate that systems with poor observability structure also are hard to learn to control.

State-of-the-art convolutional neural network models for object detection and image classification are vulnerable to physically realizable adversarial perturbations, such as patch attacks. Existing defenses have focused, implicitly or explicitly, on single-patch attacks, leaving their sensitivity to the number of patches as an open question or rendering them computationally infeasible or inefficient against attacks consisting of multiple patches in the worst cases. In this work, we propose SpaNN, an attack detector whose computational complexity is independent of the expected number of adversarial patches. The key novelty of the proposed detector is that it builds an ensemble of binarized feature maps by applying a set of saliency thresholds to the neural activations of the first convolutional layer of the victim model. It then performs clustering on the ensemble and uses the cluster features as the input to a classifier for attack detection. Contrary to existing detectors, SpaNN does not rely on a fixed saliency threshold for identifying adversarial regions, which makes it robust against white box adversarial attacks. We evaluate SpaNN on four widely used data sets for object detection and classification, and our results show that SpaNN outperforms state-of-the-art defenses by up to 11 and 27 percentage points in the case of object detection and the case of image classification, respectively. Our code is available at https://github.com/gerkbyrd/SpaNN.

Cyber-Physical Systems (CPS) are integral to critical infrastructure, but their interconnected nature exposes them to sophisticated cyber threats. Traditional security mechanisms primarily focus on detecting direct manipulations of control signals or sensor data, leaving them vulnerable to more subtle attack vectors. This paper introduces a novel optimisation framework for modelling and executing stealthy computational delay attacks - -an attack class that subtly interferes with controller execution timing to degrade system performance while remaining undetected. Unlike conventional denial-of-service attacks, these stealthy attacks introduce delays selectively, ensuring that the controller fails to compute control signals in time without triggering standard anomaly detection mechanisms. We formulate the problem as a Mixed Integer Quadratically Constrained Programming (MIQCP) optimisation, allowing attackers to maximise system disruption while evading detection. Our framework is evaluated on two control systems: a simulated stable quadruple-tank process and a real-hardware implementation of an unstable Furuta pendulum. Experimental results demonstrate that even brief, undetected computational delays can lead to severe performance degradation and system destabilisation. These findings highlight the need for improved intrusion detection mechanisms that account for time-based threats, emphasising long-term activity monitoring and adaptive defence strategies to safeguard CPS integrity.

We present a moving target defense strategy to reduce the impact of stealthy sensor attacks on feedback systems. The defender periodically and randomly switches between thresholds from a discrete set to increase the uncertainty for the attacker and make stealthy attacks detectable. However, the defender does not know the exact goal of the attacker but only the prior of the possible attacker goals. Here, we model one period with a constant threshold as a Bayesian game and use the Bayesian Nash equilibrium concept to find the distribution for the choice of the threshold in that period, which takes the defender's uncertainty about the attacker into account. To obtain the equilibrium distribution, the defender minimizes its cost consisting of the cost for false alarms and the cost induced by the attack. We present a necessary and sufficient condition for the existence of a moving target defense and formulate a linear program to determine the moving target defense. Furthermore, we present a closed-form solution for the special case when the defender knows the attacker's goals. The results are numerically evaluated on a four-tank process.

This paper addresses the question whether model knowledge can guide a defender to appropriate decisions, or not, when an attacker intrudes into control systems. The model-based defense scheme considered in this study, namely Bayesian defense mechanism, chooses reasonable reactions through observation of the system's behavior using models of the system's stochastic dynamics, the vulnerability to be exploited, and the attacker's objective. On the other hand, rational attackers take deceptive strategies for misleading the defender into making inappropriate decisions. In this paper, their dynamic decision making is formulated as a stochastic signaling game. It is shown that the belief of the true scenario has a limit in a stochastic sense at an equilibrium based on martingale analysis. This fact implies that there are only two possible cases: the defender asymptotically detects the attack with a firm belief, or the attacker takes actions such that the system's behavior becomes nominal after a finite number of time steps. Consequently, if different scenarios result in different stochastic behaviors, the Bayesian defense mechanism guarantees the system to be secure in an asymptotic manner provided that effective countermeasures are implemented. As an application of the finding, a defensive deception utilizing asymmetric recognition of vulnerabilities exploited by the attacker is analyzed. It is shown that the attacker possibly withdraws even if the defender is unaware of the exploited vulnerabilities, as long as the defender's unawareness is concealed by the defensive deception.

In this paper, we present a novel statistical convergence analysis for bilinear parameter estimators. We account for two variations of a two-stage separation technique introduced by Bai [1], where the variations differ in the second stage. It turns out for both estimators that the probability of a large error decreases as the inverse square root of the number of measurements. We numerically demonstrate the estimators' performance by solving a water leak localization problem involving bilinear parameter estimation.