Metadata only
Date
2022Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Anomaly detection among a large number of processes arises in many applications ranging from dynamic spectrum access to cybersecurity. In such problems one can often obtain noisy observations aggregated from a chosen subset of processes that conforms to a tree structure. The distribution of these observations, based on which the presence of anomalies is detected, may be only partially known. This gives rise to the need for a search strategy designed to account for both the sample complexity and the detection accuracy, as well as cope with statistical models that are known only up to some missing parameters. In this work we propose a sequential search strategy using two variations of the Generalized Log Likelihood Ratio statistic. Our proposed Hierarchical Dynamic Search (HDS) strategy is shown to be order-optimal with respect to the size of the search space and asymptotically optimal with respect to the detection accuracy. An explicit upper bound on the error probability of HDS is established for the finite sample regime. Extensive experiments are conducted, demonstrating the performance gains of HDS over existing methods. Show more
Publication status
publishedExternal links
Book title
2022 IEEE International Symposium on Information Theory (ISIT)Pages / Article No.
Publisher
IEEEEvent
Subject
Upper bound; Error probability; Dynamic spectrum access; Performance gain; Search problems; Distance measurement; Noise measurementOrganisational unit
03568 - Loeliger, Hans-Andrea / Loeliger, Hans-Andrea
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ETH Bibliography
yes
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