Digital forensics

About: Digital forensics is a research topic. Over the lifetime, 4270 publications have been published within this topic receiving 49676 citations. The topic is also known as: digital forensic science & Digital forensics.

Using Coupled Hidden Markov Models to Model Suspect Interactions in Digital Forensic Analysis

Abstract: This paper expands the use of Hidden Markov Models in Digital Forensics by using Coupled Hidden Markov Models to investigate interactions between multiple suspects in forensic cases. This paper compares the output of a coupled hidden Markov model to a similarly trained single-chain hidden Markov models and to expert knowledge in a simulated digital forensic case with two suspects. In the situation modelled, there was a notable improvement in the accuracy of the coupled model compared to single chain models. This demonstrates that there is some form of interaction between suspects in this digital forensic case, and that this interaction can be effectively modelled by a coupled hidden Markov model.

Enhanced Timeline Analysis for Digital Forensic Investigations

Abstract: Forensic analysis is the science of collecting, examining, and presenting evidence in order to support or refute a hypothesis With the increasing size of storage devices and growing popularity of digital hand-held devices connecting to the Internet, performing an effective digital forensic investigation is becoming more challenging to investigators In this article, we evaluate the existing tools of timeline analysis and identify the need for a solid timeline analysis tool For this reason, the article studies an existing but discontinued project called Zeitline, presents its features and shortcomings, and develops new capabilities to overcome these limitations A case study is then presented in which the application’s functionality is tested

Face Verification and Recognition for Digital Forensics and Information Security

Abstract: In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field.

Extraction of Common Concepts for the Mobile Forensics Domain

Abstract: With the rapid development of technology, mobile devices have become an essential tool in terms of crime fighting and criminal investigation. However, many mobile forensics investigators face difficulties with the forensics investigation process in their domain. The difficulties are due to the heavy reliance of the forensics field on knowledge as a valuable resource, a resource that is scattered and widely dispersed. Wide dispersion of mobile forensics knowledge not only makes investigation difficult for new investigators, resulting in substantial waste of time, but also leads to confusion in concepts and terminologies of mobile forensics domain. This paper proposes a common concept for the mobile forensics domain based on the concepts extraction process. The proposed concepts contribute to simplifying the investigation process and enables investigation teams to capture and reuse specialized forensic knowledge, thereby reducing the conceptual and terminological confusion in the mobile forensics domain.