2020
DOI: 10.1111/rssa.12549
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Quantifying the Association Between Discrete Event Time Series with Applications to Digital Forensics

Abstract: We consider the problem of quantifying the degree of association between pairs of discrete event time series, with potential applications in forensic and cybersecurity settings. We focus in particular on the case where two associated event series exhibit temporal clustering such that the occurrence of one type of event at a particular time increases the likelihood that an event of the other type will also occur nearby in time.We pursue a non-parametric approach to the problem and investigate various score func… Show more

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Cited by 11 publications
(5 citation statements)
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“…Although there are several other stop types, they have not been modelled because they have been considered as non-controllable or of insignificant impact on the KPIs, and according to a preliminary statistical stop correlation analysis based on [ 78 ], there was no (reliable) conclusion about their dependence with the important failure types mentioned above. Thus, for simulation purposes, the production time is uniformly distributed outside intervals of important failures and application of strategies.…”
Section: Resultsmentioning
confidence: 99%
“…Although there are several other stop types, they have not been modelled because they have been considered as non-controllable or of insignificant impact on the KPIs, and according to a preliminary statistical stop correlation analysis based on [ 78 ], there was no (reliable) conclusion about their dependence with the important failure types mentioned above. Thus, for simulation purposes, the production time is uniformly distributed outside intervals of important failures and application of strategies.…”
Section: Resultsmentioning
confidence: 99%
“…Digital forensics is a branch of forensic science that deals with the gathering and examination of digital and multimedia evidence. An emerging area of interest in digital forensics focuses on the examination of user-generated event data resulting from actions taken on or with a digital device, such as web browsing and authentication data [1], geolocation data [2][3][4], or sensor-based health and activity data [5][6][7].…”
Section: Introductionmentioning
confidence: 99%
“…LRs have been widely applied in the context of DNA evidence [13], and there is ongoing research into constructing LR models for a wide variety of other evidence types [14][15][16][17][18][19][20]. For quantitative evaluations using user-generated digital event data, recent work has also adopted the LR framework [1][2][3]8].…”
Section: Introductionmentioning
confidence: 99%
“…A number of authors have advocated for the LR approach (Aitken and Taroni, 2004;Willis et al, 2015). Attempts have been made to apply the LR approach to many kinds of forensic evidence like DNA (Steele and Balding, 2014), latent prints (Neumann et al, 2012), digital (Galbraith and Smyth, 2017;Galbraith et al, 2020), firearms (Bunch and Wevers, 2013) and handwriting (Bozza et al, 2008;Marquis et al, 2011;Gaborini et al, 2017). We briefly explain the approach here.…”
Section: Introductionmentioning
confidence: 99%