Statistical Methods for the Forensic Analysis of Geolocated Event Data

Galbraith, Christopher; Smyth, Padhraic

doi:10.1016/j.fsidi.2020.301009

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…We summarize (1) the number of users that remain after filtering, (2) the number of categories K, and (3) the definition of the categories in Table 1.…”

Section: Simulating the Stolen Device Scenariomentioning

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%

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Likelihood ratios for changepoints in categorical event data with applications in digital forensics

Longjohn,

Smyth

2024

Journal of Forensic Sciences

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We investigate likelihood ratio models motivated by digital forensics problems involving time‐stamped user‐generated event data from a device or account. Of specific interest are scenarios where the data may have been generated by a single individual (the device/account owner) or by two different individuals (the device/account owner and someone else), such as instances in which an account was hacked or a device was stolen before being associated with a crime. Existing likelihood ratio methods in this context require that a precise time is specified at which the device or account is purported to have changed hands (the changepoint)—this is the known changepoint likelihood ratio model. In this paper, we develop a likelihood ratio model that instead accommodates uncertainty in the changepoint using Bayesian techniques, that is, an unknown changepoint likelihood ratio model. We show that the likelihood ratio in this case can be calculated in closed form as an expression that is straightforward to compute. In experiments with simulated changepoints using real‐world data sets, the results demonstrate that the unknown changepoint model attains comparable performance to the known changepoint model that uses a perfectly specified changepoint, and considerably outperforms the known changepoint model that uses a misspecified changepoint, illustrating the benefit of capturing uncertainty in the changepoint.

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“…We summarize (1) the number of users that remain after filtering, (2) the number of categories K, and (3) the definition of the categories in Table 1.…”

Section: Simulating the Stolen Device Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Likelihood ratios for changepoints in categorical event data with applications in digital forensics

Longjohn,

Smyth

2024

Journal of Forensic Sciences

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Increasing Street Credibility: Cartography-Aware Forensic Analysis of GNSS Trace Validity

Hammer,

Cerny,

Jung

et al. 2024

Digital Threats

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Geolocation data recorded by consumer electronics is usually considered very helpful in criminal investigations: Every few steps, every few seconds, the location of a suspect, victim, witness or others can be pinpointed as it was automatically recorded in the background. Compared to the commonly used cell tower location data, device-local data from a global navigation satellite system (GNSS) has far higher precision (both spatial and temporal), but suffers from a lack of trust, because data provenance is under potential control of the user. In this paper, we propose two approaches of validating and analyzing such data with high confidence. The first approach formulates and checks internal consistency criteria of GNSS data, while the second approach also takes external data sources about the surrounding environment in the form of OpenStreetMap data into account. In both approaches, we formalize the concept of a data anomaly and argue that an absence of anomalies implies more trustworthy data and thus higher evidential value. This way, the vast information contained in high-density location data may actually lead to more detailed insights instead of only increasing data noise in investigations.

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Statistical Methods for the Forensic Analysis of Geolocated Event Data

Cited by 2 publications

References 13 publications

Likelihood ratios for changepoints in categorical event data with applications in digital forensics

Likelihood ratios for changepoints in categorical event data with applications in digital forensics

Increasing Street Credibility: Cartography-Aware Forensic Analysis of GNSS Trace Validity

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