Imperial College London

Professor Nick Heard

Faculty of Natural SciencesDepartment of Mathematics

Chair in Statistics



+44 (0)20 7594 1490n.heard Website




543Huxley BuildingSouth Kensington Campus






BibTex format

author = {Price-Williams, M and Heard, N},
doi = {10.1007/s11222-019-09875-z},
journal = {Statistics and Computing},
pages = {1--12},
title = {Nonparametric self-exciting models for computer network traffic},
url = {},
year = {2019}

RIS format (EndNote, RefMan)

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 - 12
PY - 2019///
SN - 0960-3174
SP - 1
TI - Nonparametric self-exciting models for computer network traffic
T2 - Statistics and Computing
UR -
UR -
ER -