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Found 18 papers, 0 papers with code
Quickest Detection of False Data Injection Attack in Distributed Process Tracking
In the Bayesian setting where there is a known prior distribution of the attack beginning instant, we formulate a Bayesian quickest change detection (QCD) problem for FDI detection in order to minimize the mean detection delay subject to a false alarm probability constraint.
Inverse Reinforcement Learning With Constraint Recovery
In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for constrained Markov decision process (CMDP) problems.
Inverse Unscented Kalman Filter
In this setting, a cognitive `adversary' tracks its target of interest via a stochastic framework such as a Kalman filter (KF).
Inverse Cubature and Quadrature Kalman filters
Recent developments in counter-adversarial system research have led to the development of inverse stochastic filters that are employed by a defender to infer the information its adversary may have learned.
Online Reinforcement Learning in Periodic MDP
We study learning in periodic Markov Decision Process (MDP), a special type of non-stationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting.
Multi-target Range and Angle detection for MIMO-FMCW radar with limited antennas
In this context, we propose a novel multi-target localization algorithm in the range-angle domain for a MIMO FMCW radar with a sparse array of randomly placed transmit and receive elements.
Counter-Adversarial Learning with Inverse Unscented Kalman Filter
In this paper, we address this scenario by formulating inverse cognition as a nonlinear Gaussian state-space model, wherein the adversary employs an unscented Kalman filter (UKF) to estimate the defender's state with reduced linearization errors.
Inverse Extended Kalman Filter -- Part II: Highly Non-Linear and Uncertain Systems
The purpose of this paper and the companion paper (Part I) is to address the inverse filtering problem in non-linear systems by proposing an inverse extended Kalman filter (I-EKF).
Online Reinforcement Learning for Periodic MDP
We study learning in periodic Markov Decision Process(MDP), a special type of non-stationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting.
Design and Detection of Controller Manipulation Attack on RIS Assisted Communication
In the third case, non-parametric Kolmogorov-Smirnov test is further simplified to a simple per-sample double threshold test.
Age-of-information minimization via opportunistic sampling by an energy harvesting source
setting, after probing a channel, the optimal source node sampling policy is shown to be a threshold policy involving the instantaneous age of the process, the available energy in the buffer and the instantaneous channel quality as the decision variables.
Inverse Extended Kalman Filter -- Part I: Fundamentals
The purpose of this paper and the companion paper (Part II) is to develop the theory of I-EKF in detail.
Compressive Sensing Based Adaptive Defence Against Adversarial Images
This paper proposes a novel Compressive sensing based Adaptive Defence (CAD) algorithm which combats distortion in frequency domain instead of time domain.
A Low-Delay MAC for IoT Applications: Decentralized Optimal Scheduling of Queues without Explicit State Information Sharing
Limiting the policy to this class reduces the problem to obtaining a queue switching policy at queue emptiness instants.
Design of false data injection attack on distributed process estimation
Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes, via Kalman-consensus filtering.
Centralized active tracking of a Markov chain with unknown dynamics
A Lagrangian relaxation of the problem is solved by an artful blending of two tools: Gibbs sampling for MSE minimization and an on-line version of expectation maximization (EM) to estimate the unknown TPM.
Quickest Bayesian and non-Bayesian detection of false data injection attack in remote state estimation
The quickest attack detection problem for a known {\em linear} attack scheme in the Bayesian setting with a Geometric prior on the attack initiation instant is posed as a constrained Markov decision process (MDP), in order to minimize the expected detection delay subject to a false alarm constraint, with the state involving the probability belief at the estimator that the system is under attack.
Efficient detection of adversarial images
In such attacks, some or all pixel values of an image are modified by an external attacker, so that the change is almost invisible to the human eye but significant enough for a DNN-based classifier to misclassify it.
