arXiv subjects

Arpan Chattopadhyay

Publications and source records attributed to Arpan Chattopadhyay.

At least 37 records · Page 2Linked to original sources

arXiv2023

Counter-Adversarial Learning with Inverse Unscented Kalman Filter

In counter-adversarial systems, to infer the strategy of an intelligent adversarial agent, the defender agent needs to cognitively sense the information that the adversary has gathered about the latter. Prior works on the problem employ linear Gaussian state-space models and solve this inverse cognition problem by designing inverse stochastic filters. However, in practice, counter-adversarial systems are generally highly nonlinear. 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. To estimate the adversary's estimate of the defender, we propose and develop an inverse UKF (IUKF) system. We then derive theoretical guarantees for the stochastic stability of IUKF in the mean-squared boundedness sense. Numerical experiments for multiple practical applications show that the estimation error of IUKF converges and closely follows the recursive Cramér-Rao lower bound.

math.OC↗

arXiv2023

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. We formulate the problem as a stationary MDP by augmenting the state space with the period index, and propose a periodic upper confidence bound reinforcement learning-2 (PUCRL2) algorithm. We show that the regret of PUCRL2 varies linearly with the period $N$ and as $\mathcal{O}(\sqrt{Tlog T})$ with the horizon length $T$. Utilizing the information about the sparsity of transition matrix of augmented MDP, we propose another algorithm PUCRLB which enhances upon PUCRL2, both in terms of regret ($O(\sqrt{N})$ dependency on period) and empirical performance. Finally, we propose two other algorithms U-PUCRL2 and U-PUCRLB for extended uncertainty in the environment in which the period is unknown but a set of candidate periods are known. Numerical results demonstrate the efficacy of all the algorithms.

cs.LG↗

arXiv2023

Multi-target Range and Angle detection for MIMO-FMCW radar with limited antennas

Multiple-input multiple-output (MIMO) radar has several advantages with respect to the traditional radar array systems in terms of performance and flexibility. However, in order to achieve high angular resolution, a MIMO radar requires a large number of transmit and receive antennas, which increases hardware design and computational complexities. Although spatial compressive sensing (CS) has been recently considered for a pulsed-waveform MIMO radar with sparse random arrays, such methods for the frequency-modulated continuous wave (FMCW) radar remain largely unexplored. 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. In particular, we first obtain the targets' range-delays using a discrete Fourier transform (DFT)-based focusing operation. The target angles are then recovered at each detected range using CS-based techniques exploiting the sparsity of the target scene. Our simulation results demonstrate the effectiveness of the proposed algorithm over the classical methods in detecting multiple targets with a sparse array.

eess.SP↗

arXiv2022

Online Reinforcement Learning for Periodic MDP

cs.LG↗

arXiv2022

OptM3Sec: Optimizing Multicast IRS-Aided Multiantenna DFRC Secrecy Channel with Multiple Eavesdroppers

With the use of common signaling methods for dual-function radar-communications (DFRC) systems, the susceptibility of eavesdropping on messages aimed at legitimate users has worsened. For DFRC systems, the radar target may act as an eavesdropper (ED) that receives a high-energy signal thereby leading to additional challenges. Unlike prior works, we consider a multicast multi-antenna DFRC system with multiple EDs. We then propose a physical layer design approach to maximize the secrecy rate by installing intelligent reflecting surfaces in the radar channels. Our optimization of multiple ED multicast multi-antenna DFRC secrecy rate (OptM3Sec) approach solves this highly nonconvex problem with respect to the precoding matrices. Our numerical experiments demonstrate the feasibility of our algorithm in maximizing the secrecy rate in this DFRC setup.

cs.IT↗

arXiv2022

Quickest Bayesian and non-Bayesian detection of false data injection attack in remote state estimation

In this paper, quickest detection of false data injection attack on remote state estimation is considered. A set of $N$ sensors make noisy linear observations of a discrete-time linear process with Gaussian noise, and report the observations to a remote estimator. The challenge is the presence of a few potentially malicious sensors which can start strategically manipulating their observations at a random time in order to skew the estimates. 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. State transition probabilities are derived in terms of system parameters, and the structure of the optimal policy is derived analytically. It turns out that the optimal policy amounts to checking whether the probability belief exceeds a threshold. Next, generalized CUSUM based attack detection algorithm is proposed for the non-Bayesian setting where the attacker chooses the attack initiation instant in a particularly adversarial manner. It turns out that computing the statistic for the generalised CUSUM test in this setting relies on the same techniques developed to compute the state transition probabilities of the MDP. Numerical results demonstrate significant performance gain under the proposed algorithms against competing algorithms.

eess.SY↗

arXiv2021

Compressive Sensing Based Adaptive Defence Against Adversarial Images

Herein, security of deep neural network against adversarial attack is considered. Existing compressive sensing based defence schemes assume that adversarial perturbations are usually on high frequency components, whereas recently it has been shown that low frequency perturbations are more effective. This paper proposes a novel Compressive sensing based Adaptive Defence (CAD) algorithm which combats distortion in frequency domain instead of time domain. Unlike existing literature, the proposed CAD algorithm does not use information about the type of attack such as l0, l2, l-infinity etc. CAD algorithm uses exponential weight algorithm for exploration and exploitation to identify the type of attack, compressive sampling matching pursuit (CoSaMP) to recover the coefficients in spectral domain, and modified basis pursuit using a novel constraint for l0, l-infinity norm attack. Tight performance bounds for various recovery schemes meant for various attack types are also provided. Experimental results against five state-of-the-art white box attacks on MNIST and CIFAR-10 show that the proposed CAD algorithm achieves excellent classification accuracy and generates good quality reconstructed image with much lower computation

eess.IV↗

arXiv2021

Minimization of Age-of-Information in Remote Sensing with Energy Harvesting

In this paper, the minimization of time-averaged age-of-information (AoI) in an energy harvesting (EH) source-equipped remote sensing setting is considered. The EH source opportunistically samples one or multiple processes over discrete time instants and sends the status updates to a sink node over a time-varying wireless link. At any discrete-time instant, the EH node decides whether to probe the link quality using its stored energy and further decides whether to sample a process and communicate the data based on the channel probe outcome. The trade-off is between the freshness of information available at the sink node and the available energy at the energy buffer of the source node. To this end, an infinite horizon Markov decision process theory is used to formulate the problem of minimization of time-averaged expected AoI for a single energy harvesting source node. The following two scenarios are considered: (i) single process with channel state information at the transmitter (CSIT), (ii) multiple processes with CSIT. In each scenario, for probed channel state, the optimal source node sampling policy is shown to be a threshold policy involving the instantaneous age of the process(es), the available energy in the buffer, and the instantaneous channel quality as the decision variables. Finally, numerical results are provided to demonstrate the policy structures and trade-offs.

cs.IT↗

arXiv2021

Design of false data injection attack on distributed process estimation

Herein, design of false data injection attack on a distributed cyber-physical system is considered. A stochastic process with linear dynamics and Gaussian noise is measured by multiple agent nodes, each equipped with multiple sensors. The agent nodes form a multi-hop network among themselves. Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes, via Kalman-consensus filtering. An external attacker, capable of arbitrarily manipulating the sensor observations of some or all agent nodes, injects errors into those sensor observations. The goal of the attacker is to steer the estimates at the agent nodes as close as possible to a pre-specified value, while respecting a constraint on the attack detection probability. To this end, a constrained optimization problem is formulated to find the optimal parameter values of a certain class of linear attacks. The parameters of linear attack are learnt on-line via a combination of stochastic approximation based update of a Lagrange multiplier, and an optimization technique involving either the Karush-Kuhn-Tucker (KKT) conditions or online stochastic gradient descent. The problem turns out to be convex for some special cases. Desired convergence of the proposed algorithms are proved by exploiting the convexity and properties of stochastic approximation algorithms. Finally, numerical results demonstrate the efficacy of the attack.

eess.SY↗

arXiv2020

Local Relay Selection in Presence of Dynamic Obstacles in Millimeter Wave D2D Communication

Blockage due to obstacles in millimeter wave (mmWave) device to device (D2D) communication is a prominent problem due to their severe penetration losses. Potential user equipments (UEs) in vicinity of the source UE must be explored in order to select a new relay when the current link gets blocked. However, dynamic obstacles are not known in advance and thus may cause unpredictable fluctuations to D2D channel quality causing newly selected relay link also to be susceptible to blockage. This might cause frequent relay switching leading to call drops and high energy consumption. We have proposed the idea of reducing frequency in relay exploration and switching and thus average end-to-end delay (in seconds) at the expense of additional exploration time units (few milliseconds) during beam alignment. We seek to learn the uncertainty in D2D link qualities by modeling the problem as finite horizon partially observable Markov decision process (POMDP) framework locally at each UE. We have derived an optimal threshold policy which maps the state to set of actions. We then give a simplified and easy to implement stationary threshold policy which counts the number of successive acknowledgment successes/failures for making decisions of selecting or not selecting a given relay locally. Through extensive simulation, we validate our theoretical findings and demonstrate that our approach captures the trade-off between average exploration time and average end-to-end (E2E) delay in presence of dynamic obstacles.

cs.NI↗

arXiv2020

Centralized active tracking of a Markov chain with unknown dynamics

In this paper, selection of an active sensor subset for tracking a discrete time, finite state Markov chain having an unknown transition probability matrix (TPM) is considered. A total of N sensors are available for making observations of the Markov chain, out of which a subset of sensors are activated each time in order to perform reliable estimation of the process. The trade-off is between activating more sensors to gather more observations for the remote estimation, and restricting sensor usage in order to save energy and bandwidth consumption. The problem is formulated as a constrained minimization problem, where the objective is the long-run averaged mean-squared error (MSE) in estimation, and the constraint is on sensor activation rate. 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. Finally, the Lagrange multiplier is updated using slower timescale stochastic approximation in order to satisfy the sensor activation rate constraint. The on-line EM algorithm, though adapted from literature, can estimate vector-valued parameters even under time-varying dimension of the sensor observations. Numerical results demonstrate approximately 1 dB better error performance than uniform sensor sampling and comparable error performance (within 2 dB bound) against complete sensor observation. This makes the proposed algorithm amenable to practical implementation.

cs.LG↗

arXiv2020

Efficient detection of adversarial images

In this paper, detection of deception attack on deep neural network (DNN) based image classification in autonomous and cyber-physical systems is considered. Several studies have shown the vulnerability of DNN to malicious deception attacks. 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. This paper first proposes a novel pre-processing technique that facilitates the detection of such modified images under any DNN-based image classifier as well as the attacker model. The proposed pre-processing algorithm involves a certain combination of principal component analysis (PCA)-based decomposition of the image, and random perturbation based detection to reduce computational complexity. Next, an adaptive version of this algorithm is proposed where a random number of perturbations are chosen adaptively using a doubly-threshold policy, and the threshold values are learnt via stochastic approximation in order to minimize the expected number of perturbations subject to constraints on the false alarm and missed detection probabilities. Numerical experiments show that the proposed detection scheme outperforms a competing algorithm while achieving reasonably low computational complexity.

eess.IV↗

arXiv2020

Distributed Relay Selection in Presence of Dynamic Obstacles in Millimeter Wave D2D Communication

Millimeter wave (mmWave) device to device (D2D) communication is highly susceptible to obstacles due to severe penetration losses and requires almost a line of sight (LOS) communication path. D2D channel condition is local to devices/user equipments (UEs) and hence is \textit{not} directly visible to the base station (BS). Thus quality of the D2D channel needs to be propagated to BS by UEs which may incur some delay. Hence the solution provided by BS to UEs using this gathered channel information might become less useful to establish communication due to moving obstacles. These types of obstacles might not be known in advance and hence may cause unpredictable fluctuations to the D2D channel quality. Hence we seek to learn the D2D channels using the finite horizon partially observable Markov decision process (POMDP) framework to model the uncertainty in such kind of network environments with dynamic obstacles. The objective is to minimize delay when channel quality deteriorates, by making UEs choose locally the best possible decision between i) to continue on the current relay link on which communication is taking place or ii) to switch to another good relay by exploring other possible UEs in its locality. We derive an optimal threshold policy which tells the UE to take appropriate decision locally. Later, we give a simplified and easy to implement stationary threshold policy which counts the number of successive acknowledgement failures, based on which UE make appropriate decision locally. Through extensive simulation, we demonstrate that our approach outperforms recent algorithms.

cs.NI↗

arXiv2020

Optimal deception attack on networked vehicular cyber physical systems

Herein, design of false data injection attack on a distributed cyber-physical system is considered. A stochastic process with linear dynamics and Gaussian noise is measured by multiple agent nodes, each equipped with multiple sensors. The agent nodes form a multi-hop network among themselves. Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes,via Kalman-consensus filtering. An external attacker, capable of arbitrarily manipulating the sensor observations of some or all agent nodes, injects errors into those sensor observations. The goal of the attacker is to steer the estimates at the agent nodes as close as possible to a pre-specified value, while respecting a constraint on the attack detection probability. To this end,a constrained optimization problem is formulated to find the optimal parameter values of a certain class of linear attacks. The parameters of linear attack are learnt on-line via a combination of stochastic approximation and online stochastic gradient descent.Numerical results demonstrate the efficacy of the attack.

eess.SY↗

arXiv2018

Location Aware Opportunistic Bandwidth Sharing between Static and Mobile Users with Stochastic Learning in Cellular Networks

We consider location-dependent opportunistic bandwidth sharing between static and mobile downlink users in a cellular network. Each cell has some fixed number of static users. Mobile users enter the cell, move inside the cell for some time and then leave the cell. In order to provide higher data rate to mobile users, we propose to provide higher bandwidth to the mobile users at favourable times and locations, and provide higher bandwidth to the static users in other times. We formulate the problem as a long run average reward Markov decision process (MDP) where the per-step reward is a linear combination of instantaneous data volumes received by static and mobile users, and find the optimal policy. The transition structure of this MDP is not known in general. To alleviate this issue, we propose a learning algorithm based on single timescale stochastic approximation. Also, noting that the unconstrained MDP can be used to solve a constrained problem, we provide a learning algorithm based on multi-timescale stochastic approximation. The results are extended to address the issue of fair bandwidth sharing between the two classes of users. Numerical results demonstrate performance improvement by our scheme, and also the trade-off between performance gain and fairness.

cs.NI↗

arXiv2018

Security against false data injection attack in cyber-physical systems

In this paper, secure, remote estimation of a linear Gaussian process via observations at multiple sensors is considered. Such a framework is relevant to many cyber-physical systems and internet-of-things applications. Sensors make sequential measurements that are shared with a fusion center; the fusion center applies a certain filtering algorithm to make its estimates. The challenge is the presence of a few unknown malicious sensors which can inject anomalous observations to skew the estimates at the fusion center. The set of malicious sensors may be time-varying. The problems of malicious sensor detection and secure estimation are considered. First, an algorithm for secure estimation is proposed. The proposed estimation scheme uses a novel filtering and learning algorithm, where an optimal filter is learnt over time by using the sensor observations in order to filter out malicious sensor observations while retaining other sensor measurements. Next, a novel detector to detect injection attacks on an unknown sensor subset is developed. Numerical results demonstrate up to 3 dB gain in the mean squared error and up to 75% higher attack detection probability under a small false alarm rate constraint, against a competing algorithm that requires additional side information.

cs.CR↗

arXiv2018

Dynamic Sensor Subset Selection for Centralized Tracking a Time-Varying Stochastic Process

Motivated by the Internet-of-things and sensor networks for cyberphysical systems, the problem of dynamic sensor activation for the centralized tracking of an i.i.d. time-varying process is examined. The tradeoff is between energy efficiency, which decreases with the number of active sensors, and fidelity, which increases with the number of active sensors. The problem of minimizing the time-averaged mean-squared error over infinite horizon is examined under the constraint of the mean number of active sensors. The proposed methods artfully combine Gibbs sampling and stochastic approximation for learning, in order to create a high performance, energy efficient tracking mechanisms with active sensor selection. Centralized tracking of i.i.d. process with known distribution as well as an unknown parametric distribution are considered. For an i.i.d. process with known distribution, convergence to the global optimal solution with high probability is proved. The main challenge of the i.i.d. case is that the process has a distribution parameterized by a known or unknown parameter which must be learned; one key theoretical result proves that the proposed algorithm for tracking an i.i.d. process with unknown parametric distribution converges to local optima. Numerical results show the efficacy of the proposed algorithms and also suggest that global optimality is in fact achieved in some cases.

math.OC↗

arXiv2017

Optimal Dynamic Sensor Subset Selection for Tracking a Time-Varying Stochastic Process

Motivated by the Internet-of-things and sensor networks for cyberphysical systems, the problem of dynamic sensor activation for the tracking of a time-varying process is examined. The tradeoff is between energy efficiency, which decreases with the number of active sensors, and fidelity, which increases with the number of active sensors. The problem of minimizing the time-averaged mean-squared error over infinite horizon is examined under the constraint of the mean number of active sensors. The proposed methods artfully combine three key ingredients: Gibbs sampling, stochastic approximation for learning, and modifications to consensus algorithms to create a high performance, energy efficient tracking mechanisms with active sensor selection. The following progression of scenarios are considered: centralized tracking of an i.i.d. process; distributed tracking of an i.i.d. process and finally distributed tracking of a Markov chain. The challenge of the i.i.d. case is that the process has a distribution parameterized by a known or unknown parameter which must be learned. The key theoretical results prove that the proposed algorithms converge to local optima for the two i.i.d process cases; numerical results suggest that global optimality is in fact achieved. The proposed distributed tracking algorithm for a Markov chain, based on Kalman-consensus filtering and stochastic approximation, is seen to offer an error performance comparable to that of a competetive centralized Kalman filter.

eess.SY↗