Attack Detection in Sensor Network Target Localization Systems with Quantized Data

We consider a sensor network focused on target localization, where sensors measure the signal strength emitted from the target. Each measurement is quantized to one bit and sent to the fusion center. A general attack is considered at some sensors that attempts to cause the fusion center to produce an inaccurate estimation of the target location with a large mean-square-error. The attack is a combination of man-in-the-middle, hacking, and spoofing attacks that can effectively change both signals going into and coming out of the sensor nodes in a realistic manner. We show that the essential effect of attacks is to alter the estimated distance between the target and each attacked sensor to a different extent, giving rise to a geometric inconsistency among the attacked and unattacked sensors. Hence, with the help of two secure sensors, a class of detectors are proposed to detect the attacked sensors by scrutinizing the existence of the geometric inconsistency. We show that the false alarm and miss probabilities of the proposed detectors decrease exponentially as the number of measurement samples increases, which implies that for sufficiently large number of samples, the proposed detectors can identify the attacked and unattacked sensors with any required accuracy

Distributed Joint Spoofing Attack Identification and Estimation in Sensor Networks

Distributed estimation of a deterministic scalar parameter by using quantized data in the presence of spoofing attacks, which modify the statistical model of the physical phenomenon, is considered. The paper develops an efficient heuristic approach to jointly detect attacks and estimate under spoofing attacks that are undetectable by a traditional approach that relies on noticing the data is not consistent with an expected family of distributions. Numerical results show that the proposed approach can correctly identify the attacked sensors with a large number of time observations, and moreover, the estimation performance of the proposed approach can asymptotically achieve the genie Cramer-Rao bound (CRB) for the desired parameter, which is the CRB under the assumption that the set of attacked sensors is known