"ADbenchmark"

"Among 5 widely used datasets in anomoly detection domain, come from those papers:Y. Su, Y. Zhao, C. Niu, et al., \u201cRobust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network,\u201d in Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2019, pp. 2828-2837.K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, \u201cDetecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding,\u201d in Proc. 24th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., July 2018, pp. 1752\u20131761, doi: 10.1145\/3219819.3219845.J. Su, D. V. Vargas, and K. Sakurai, \u201cOne pixel attack for fooling deep neural networks,\u201d IEEE Trans. Evol. Comput., vol. 23, no. 5, pp. 828\u2013841, 2019, doi: 10.1109\/TEVC.2019.2890858.A. P. Mathur and N. O. Tippenhauer, \u201cSWaT: a water treatment testbed for research and training on ICS security,\u201d in Proc. 2016 Int. Workshop Cyber-Phys. Syst. Smart Water Networks (CySWater), Apr. 2016, pp. 1\u20136, doi: 10.1109\/CySWater.2016.7469060.A. Abdulaal and T. Lancewicki, \u201cReal-Time Synchronization in Neural Networks for Multivariate Time Series Anomaly Detection,\u201d in Proc. Int. Conf. Acoustics, Speech, Signal Process., 2021."