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. 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 naive estimate of the distance between the target and each attacked sensor, which ignores the existence of attacks, to a different extent, giving rise to a geometric inconsistency among the attacked and unattacked sensors. 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 with sufficient measurement samples, the proposed detectors can identify the attacked and unattacked sensors with any required accuracy. Numerical results show that compared to the cases where all sensors are employed without detecting attacks or only the secure sensors are employed, the localization performance can be significantly improved if we employ the secure sensors and the sensors which are declared as unattacked by the proposed detector.