Biblio

We introduce a new defense mechanism for stochastic control systems with control objectives, to enhance their resilience before the detection of any attacks. To this end, we cautiously design the outputs of the sensors that monitor the state of the system since the attackers need the sensor outputs for their malicious objectives in stochastic control scenarios. Different from the defense mechanisms that seek to detect infiltration or to improve detectability of the attacks, the proposed approach seeks to minimize the damage of possible attacks before they actually have even been detected. We, specifically, consider a controlled Gauss-Markov process, where the controller could have been infiltrated into at any time within the system's operation. Within the framework of game-theoretic hierarchical equilibrium, we provide a semi-definite programming based algorithm to compute the optimal linear secure sensor outputs that enhance the resiliency of control systems prior to attack detection.

Parameter estimation in wireless sensor networks (WSN) using encrypted non-binary quantized data is studied. In a WSN, sensors transmit their observations to a fusion center through a wireless medium where the observations are susceptible to unauthorized eavesdropping. Encryption approaches for WSNs with fixed threshold binary quantization were previously explored. However, fixed threshold binary quantization limits parameter estimation to scalar parameters. In this paper, we propose a stochastic encryption approach for WSNs that can operate on non-binary quantized observations and has the capability for vector parameter estimation. We extend a binary stochastic encryption approach proposed previously, to a non-binary generalized case. Sensor outputs are quantized using a quantizer with R + 1 levels, where R $ε$ 1, 2, 3,..., encrypted by flipping them with certain flipping probabilities, and then transmitted. Optimal estimators using maximum-likelihood estimation are derived for both a legitimate fusion center (LFC) and a third party fusion center (TPFC) perspectives. We assume the TPFC is unaware of the encryption. Asymptotic analysis of the estimators is performed by deriving the Cramer-Rao lower bound for LFC estimation, and the asymptotic bias and variance for TPFC estimation. Numerical results validating the asymptotic analysis are presented.