Wang, J.Paschalidis, Ioannis Ch.2016-08-282016-08-292016-09-292016-09-292014-06J Wang, I Ch Paschalidis. 2014. "Robust Anomaly Detection in Dynamic Networks." Proceedings of the 22nd Mediterranean Conference on Control and Automation (MED 14), pp. 428 - 433.https://hdl.handle.net/2144/18020We propose two robust methods for anomaly detection in dynamic networks in which the properties of normal traffic evolve dynamically. We formulate the robust anomaly detection problem as a binary composite hypothesis testing problem and propose two methods: a model-free and a model-based one, leveraging techniques from the theory of large deviations. Both methods require a family of Probability Laws (PLs) that represent normal properties of traffic. We devise a two-step procedure to estimate this family of PLs. We compare the performance of our robust methods and their vanilla counterparts, which assume that normal traffic is stationary, on a network with a diurnal normal pattern and a common anomaly related to data exfiltration. Simulation results show that our robust methods perform better than their vanilla counterparts in dynamic networks.p. 428 - 433Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Networking and internet architectureApplicationsRobust statistical anomaly detectionProbability lawsStatistical testingArticle10.1109/MED.2014.6961410