University of Tehran Press Advances in Industrial Engineering 2783-1744 56 1 2022 01 01 Enhancing the Performance of Monitoring the DCSBM Using Multivariate Control Charts with Estimated Parameters 73 86 87335 10.22059/aie.2022.339650.1828 EN Sara Abossedgh Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. Abbas Saghaei Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. Amirhossein Amiri Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran. 0000-0002-2385-8910 Journal Article 2022 02 27 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. https://aie.ut.ac.ir/article_87335_308b6b5a9bd75be709ea07e93bcae121.pdf