enhancing the performance of monitoring the dcsbm using multivariate control charts with estimated parameters

Many methods are applied to network surveillance for anomaly detection. some quality control methods have been developed to monitor several quality characteristics simultaneously in different networks. in our study, we use three multivariate process monitoring techniques such as hotelling’s t2, mewma, and mcusum to compare to the prior univariate control charts in the degree-corrected stochastic block model (dcsbm), a random network model supporting the degree of each node based on poisson distribution. by estimating parameters in phase i from many charts, we apply arl and sdrl metrics for the performance evaluation of multivariate control charts. the advantage of our method is detecting signals faster than previews ones by simulation and this is useful for defining the suitable method in different types of change. furthermore, the quality of performance in different multivariate methods is displayed in detecting the shifts in the dcsbm. finally, mcusum shows better performance for monitoring local and global changes than other methods.