by Ciamarra MP, Coniglio A, DE ARCANGELIS Lucilla
Abstract:
The most costly and annoying characteristic of the e-mail communication system is the large number of unsolicited commercial e-mails, known as spams, that are continuously received. Via the investigation of the statistical properties of the spam delivering intertimes, we show that spams delivered to a given recipient are time correlated: if the intertime between two consecutive spams is small (large), then the next spam will most probably arrive after a small (large) intertime. Spam temporal correlations are reproduced by a numerical model based on the random superposition of spam sequences, each one described by the Omori law. This and other experimental findings suggest that statistical approaches may be used to infer how spammers operate. Copyright (c) EPLA, 2008
Reference:
Correlations and Omori law in spamming (Ciamarra MP, Coniglio A, DE ARCANGELIS Lucilla), In EUROPHYSICS LETTERS, volume 84, 2008. (Articolo in rivista)
Bibtex Entry:
@article{mpc08,
author = {Ciamarra MP, and Coniglio A, and DE ARCANGELIS Lucilla,},
title = {Correlations and Omori law in spamming},
volume = {84},
note = {Articolo in rivista},
issn = {0295-5075},
journal = {EUROPHYSICS LETTERS},
doi = {10.1209/0295-5075/84/28004},
year = {2008},
wosId = {000263598500039},
scopusId = {2-s2.0-79051469632},
abstract = {The most costly and annoying characteristic of the e-mail communication system is the large number of unsolicited commercial e-mails, known as spams, that are continuously received. Via the investigation of the statistical properties of the spam delivering intertimes, we show that spams delivered to a given recipient are time correlated: if the intertime between two consecutive spams is small (large), then the next spam will most probably arrive after a small (large) intertime. Spam temporal correlations are reproduced by a numerical model based on the random superposition of spam sequences, each one described by the Omori law. This and other experimental findings suggest that statistical approaches may be used to infer how spammers operate. Copyright (c) EPLA, 2008}
}