The problem of secure quantized target tracking in wireless sensor networks (WSN) is investigated. Due to the limited energy supplies of nodes in WSN, optimizing their design under energy constraints, reducing their communication costs, securing their data aggregation are of paramount importance. To this goal and in order to efficiently solve the problem of target tracking in WSN with quantized measurements, we propose a new method for jointly selecting the appropriate group of candidate sensors that participate in data collection, detecting the malicious sensors and estimating the target position based on quantized proximity sensors. Firstly, we select the best group in order to provide the required data of the target and to balance the energy dissipation in the WSN. This selection is also based on the transmission power between one sensor and the cluster head. Secondly, we detect the malicious sensor nodes from learned data based on the information relevance of their measurements. Then, we estimate the target position using Quantized Variational Filtering (QVF) algorithm. The performance of the proposed method is validated by simulation results in target tracking for WSN.