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BibTex format

@article{Price-Williams:2020:10.1007/s11222-019-09875-z,
author = {Price-Williams, M and Heard, N},
doi = {10.1007/s11222-019-09875-z},
journal = {Statistics and Computing},
pages = {209--220},
title = {Nonparametric self-exciting models for computer network traffic},
url = {http://dx.doi.org/10.1007/s11222-019-09875-z},
volume = {30},
year = {2020}
}

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TY  - JOUR
AB  - Connectivity patterns between nodes in a computer network can be interpreted and modelled as point processes where events in a process indicate connections being established for data to be sent along that edge. A model of normal connectivity behaviour can be constructed for each edge in a network by identifying key network user features such as seasonality or self-exciting behaviour, since events typically arise in bursts at particular times of day which may be peculiar to that edge. When monitoring a computer network in real time, unusual patterns of activity against the model of normality could indicate the presence of a malicious actor. A flexible, novel, nonparametric model for the excitation function of a Wold process is proposed for modelling the conditional intensities of network edges. This approach is shown to outperform standard seasonality and self-excitation models in predicting network connections, achieving well-calibrated predictions for event data collected from the computer networks of both Imperial College and Los Alamos National Laboratory.
AU  - Price-Williams,M
AU  - Heard,N
DO  - 10.1007/s11222-019-09875-z
EP  - 220
PY  - 2020///
SN  - 0960-3174
SP  - 209
TI  - Nonparametric self-exciting models for computer network traffic
T2  - Statistics and Computing
UR  - http://dx.doi.org/10.1007/s11222-019-09875-z
UR  - http://hdl.handle.net/10044/1/70238
VL  - 30
ER  -

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