Usage of Published Network Traffic Datasets for Anomaly and Change Point Detection

Aleksiejunas, Rimvydas; Garuolis, Deividas

doi:10.1007/s11277-023-10816-3

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2024

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“…The author uses a previously known likelihood method and suggests a new maximum distance of the running means method to identify locations of the change-points within the 2G network. One of the latest works in this area is by Aleksiejunas and Garuolis [23] and is devoted to traffic change-points; it utilizes machine learning methods such as long short-term memory (LSTM) and recurrent neural networks (RNNs). The authors apply change-point identification algorithms for synthetic data and reuse algorithms for the spatial traffic distributions of LTE (Long Term Evolution) mobile networks.…”

Section: Introductionmentioning

confidence: 99%

Change-Point Detection in Functional First-Order Auto-Regressive Models

Birbilas,

Račkauskas

2024

Mathematics

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A sample of continuous random functions with auto-regressive structures and possible change-point of the means are considered. We present test statistics for the change-point based on a functional of partial sums. To study their asymptotic behavior, we prove functional limit theorems for polygonal line processes in the space of continuous functions. For some situations, we use a block bootstrap procedure to construct the critical region and provide applications. We also study the finite sample behavior via simulations. Eventually, we apply the statistics to a telecommunications data sample.

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Section: Introductionmentioning

confidence: 99%