Robust Anomaly Detection in Dynamic Networks
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Paschalidis, Ioannis Ch.
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CitationJ 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.
We 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.