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Robust anomaly detection in dynamic networks

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J 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.Rights

Attribution 4.0 International

BU Open Access Articles -

Autor: Wang, J. - Paschalidis, Ioannis Ch. - -


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