Skip to content

Statistical Methods for Analyzing Event Time-Series Data in Digital Forensics

Type: Webinar
Research Area: Digital

This CSAFE webinar was presented by Dr. Padhraic Smyth from University of California, Irvine on September 28, 2017.

Description:
Time-series of user-generated events are routinely captured and logged on devices such as computers and mobile phones. This type of data is of increasing interest in forensic investigations. There has been relatively little use of statistical thinking in this context, for a variety of reasons. In this talk we will discuss recent work at UC Irvine (under CSAFE) that has begun to develop statistical techniques to answer basic questions about such data, building on methods such as marked point processes. We will outline the methodological approach and describe results to date on both simulated and real-world data. The talk will conclude with a brief discussion of challenges in this area as well as some thoughts on future research directions.

Related Resources

LibDroid: Summarizing information flow of Android Native Libraries via Static Analysis

LibDroid: Summarizing information flow of Android Native Libraries via Static Analysis

With advancements in technology, people are taking advantage of mobile devices to access e-mails, search the web, and video chat. Therefore, extracting evidence from mobile phones is an important component…
Evaluating Reference Sets for Score-Based Likelihood Ratios for Camera Device Identification

Evaluating Reference Sets for Score-Based Likelihood Ratios for Camera Device Identification

An investigator wants to know if an illicit image captured by an unknown camera was taken by a person of interest’s (POI’s) phone. Score-based likelihood ratios (SLRs) have been used…
Likelihood Ratios for Categorical Evidence with Applications to Digital Forensics

Likelihood Ratios for Categorical Evidence with Applications to Digital Forensics

In forensic investigations, the goal of evidence evaluation is often to address source-/identity-based questions in which the evidence consists of two sets of observations: one from an unknown source tied…
Automatic Detection of Android Steganography Apps via Symbolic Execution and Tree Matching

Automatic Detection of Android Steganography Apps via Symbolic Execution and Tree Matching

The recent focus of cyber security on automated detection of malware for Android apps has omitted the study of some apps used for “legitimate” purposes, such as steganography apps. Mobile…