Abstract: With the popularization of smart devices and the development of wireless communication technology,when users use wireless networks to meet various needs,wireless networks also record a large number of users' spatial-temporal trajectory data.Anomaly detection for spatial-temporal trajectory data becomes a new research hotspot in the field of data mining.In order to better pay attention to the healthy development of students and promote the informatization construction of campus,a spectral clustering algorithm based on the combination of multi-scalethreshold and density (MSTD-SC) is proposed,taking the real internet usage data ofcampus as an example.Firstly,it uses the affinity distance function based on the shortest time distance-shortest time distance subsequences (STD-STDSS) to construct the initial adjacency matrix.Then it introduces the covariance scale eigenvector space by threshold and spatial scale eigenvector space by threshold to perform 0-1 processing on the adjacency matrix to obtain more accurate sample similarity.Next,comstructing a eigenvalue decomposition of the adjacency matrix.Finally,it uses DBSCAN clustering algorithm to avoid to manually determine the number of clusters.Using Silhouette Index to evaluate the experimental results obtained by multiple algorithms,MSTD-SC algorithm reflects better clustering performance.Applying it to individual user anomaly detection,the abnormal user list is verified to be effective and credible.

Key words: Anomaly detection, Campus wireless network, Similarity, Spatial-temporal trajectory data, Spectral clustering