Analyzing user-event data using score-based likelihood ratios with marked point processes

Galbraith, Christopher; Smyth, Padhraic

doi:10.1016/j.diin.2017.06.009

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…This approach is often used in biometrics for example (Ross et al, 2006). Galbraith and Smyth (2017) investigated the potential of SLRs for assessing the strength of association by using a combination of near-neighbour score functions and population-based inference (see Section 5.1). In the present paper we extend these ideas to a broader and more general framework.…”

Section: Likelihood Ratio Methods In Forensic Sciencementioning

confidence: 99%

Quantifying the Association Between Discrete Event Time Series with Applications to Digital Forensics

Galbraith

Smyth

2020

Journal of the Royal Statistical Society Series A: Statistics in Society

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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 functions to quantify association, including characteristics of marked point processes and summary statistics of interevent times. Two techniques are proposed for assessing the significance of the measured degree of association: a populationbased approach to calculating score-based likelihood ratios when a sample from a relevant population is available, and a resampling approach to computing coincidental match probabilities when only a single pair of event series is available. The methods are applied to simulated data and to two real world data sets consisting of logs of computer activity and achieve accurate results across all data sets.

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Section: Likelihood Ratio Methods In Forensic Sciencementioning

confidence: 99%

Quantifying the Association Between Discrete Event Time Series with Applications to Digital Forensics

Galbraith

Smyth

2020

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…Notably, evaluation by case study allows creating a complete and complex picture of whole phenomena, as stated in [ 46 , 47 , 48 ]. This work follows the theoretical approach for constructing case studies as presented in [ 49 , 50 ]. Christopher Galbraith evaluated his work by comparing the data of 28 individuals over seven days on the known same source and known different sources [ 49 ].…”

Section: Case Studymentioning

confidence: 99%

“…This work follows the theoretical approach for constructing case studies as presented in [ 49 , 50 ]. Christopher Galbraith evaluated his work by comparing the data of 28 individuals over seven days on the known same source and known different sources [ 49 ]. Hyunji Chung et al created a hypothetical crime scene for investigation on cloud forensics.…”

Section: Case Studymentioning

confidence: 99%

A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks

Arshad

Abdullah

Alawida

et al. 2022

Sensors

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Currently, law enforcement and legal consultants are heavily utilizing social media platforms to easily access data associated with the preparators of illegitimate events. However, accessing this publicly available information for legal use is technically challenging and legally intricate due to heterogeneous and unstructured data and privacy laws, thus generating massive workloads of cognitively demanding cases for investigators. Therefore, it is critical to develop solutions and tools that can assist investigators in their work and decision making. Automating digital forensics is not exclusively a technical problem; the technical issues are always coupled with privacy and legal matters. Here, we introduce a multi-layer automation approach that addresses the automation issues from collection to evidence analysis in online social network forensics. Finally, we propose a set of analysis operators based on domain correlations. These operators can be embedded in software tools to help the investigators draw realistic conclusions. These operators are implemented using Twitter ontology and tested through a case study. This study describes a proof-of-concept approach for forensic automation on online social networks.

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“…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%

Towards a Likelihood Ratio Approach for Bloodstain Pattern Analysis

Zou

2022

SSRN Journal

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Analyzing user-event data using score-based likelihood ratios with marked point processes

Cited by 8 publications

References 16 publications

Quantifying the Association Between Discrete Event Time Series with Applications to Digital Forensics

Quantifying the Association Between Discrete Event Time Series with Applications to Digital Forensics

A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks

Towards a Likelihood Ratio Approach for Bloodstain Pattern Analysis

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