In the User-centric Ultra-Dense Network (UUDN), due to the limited length of the pilot sequence for channel estimation, the traditional eavesdropping detection technology based on information theoretic criteria cannot function ideally or is even completely invalid. In view of this, this study proposes a multi-node joint detection algorithm based on the LS-FDC criterion. This method uses the Linear Shrinkage (LS) theory in statistics to shrink and optimize the sample covariance matrix received by each node so that it can better fit the distribution of the overall eigenvalues after eigen-decomposition. The nodes in the Access Point Group (APG) jointly determine whether there are eavesdroppers with the Flexible Detection Criterion (FDC) algorithm. The simulation experiments and theoretical analysis show that compared with other algorithms for signal source estimation and detection, this algorithm has a significantly improved detection probability when the signal-to-noise ratio is low and the pilot sequence length is limited. A good detection effect can still be achieved even when the pilot sequence length is shorter than the number of node antennas.