PROJECT TITLE :

Attack Detection in Sensor Network Target Localization Systems With Quantized Data - 2018

ABSTRACT:

We have a tendency to think about a sensor network focused heading in the right direction localization, where sensors live the signal strength emitted from the target. Every measurement is quantized to one bit and sent to the fusion center. A general attack is taken into account at some sensors that attempts to cause the fusion center to provide an inaccurate estimation of the target location. The attack may be a combination of man-in-the-middle, hacking, and spoofing attacks that can effectively change each signals going into and returning out of the sensor nodes in a very realistic manner. We have a tendency to show that the essential effect of attacks is to change the naive estimate of the distance between the target and every attacked sensor, which ignores the existence of attacks, to a different extent, giving rise to a geometrical inconsistency among the attacked and unattacked sensors. With the help of two secure sensors, a category of detectors are proposed to detect the attacked sensors by scrutinizing the existence of the geometric inconsistency. We have a tendency to show that the false alarm and miss chances of the proposed detectors decrease exponentially as the amount of measurement samples will increase, that implies that with sufficient measurement samples, the proposed detectors will determine the attacked and unattacked sensors with any needed accuracy. Numerical results show that compared to the cases where all sensors are utilized without detecting attacks or only the secure sensors are utilized, the localization performance can be considerably improved if we tend to employ the secure sensors and the sensors which are declared as unattacked by the proposed detector.