Doctor of Philosophy
Electrical & Computer Engineering
Dr. Ruixin Niu
In this dissertation, we first investigate the problem of source location estimation in wireless sensor networks (WSNs) based on quantized data in the presence of false information attacks. Using a Gaussian mixture to model the possible attacks, we develop a maximum likelihood estimator (MLE) to estimate the source location. The Cramer-Rao lower bound (CRLB) for this estimation problem is also derived.
Then, the assumption that the fusion center does not have the knowledge of the attack probability and the attack noise power investigated. We assume that the attack probability and power are random variables which follow certain uniform distributions. We derive the MLE for the localization problem. The CRLB for this estimation problem is also derived. It is shown that the proposed estimator is robust in various cases with different attack probabilities and parameter mismatch.
The linear state estimation problem subjected to False Information Injection is also considered. The relationship between the attacker and the defender is modeled from a minimax perspective, in which the attacker tries to maximize the cost function. On the other hand, the defender tries to optimize the detection threshold selection to minimize the cost function. We consider that the attacker will attack with deterministic bias, then we also considered the random bias. In both cases, we derive the probabilities of detection and miss, and the probability of false alarm is derived based on the Chi squared distribution. We solve the minimax optimization problem numerically for both the cases.
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Available for download on Saturday, December 12, 2020