The increasing adoption of digital technologies to manage and process information used in everyday life, results in an increase in the demand for digital data analysis for investigative purposes. In fact, the reconstruction of computer and telematic crimes, or, in general, of crimes committed with computer systems, require the adoption of Computer Forensics best practices in order to extract relevant evidences from electronic devices, guaranteeing the integrity of data and their admissibility during a trial. The process of extraction, conservation, analysis and documentation of a forensic investigation can be enhanced by a framework that support investigators during their work, correlating evidences collected by different forensic tools. So, in this work we propose a semantic methodology and a system architecture for evidences correlation aiming to provide enhanced retrieval and reasoning capabilities.

Analyse digital forensic evidences through a semantic-based methodology and NLP techniques

F. Amato;G. Cozzolino;V. Moscato;
2019

Abstract

The increasing adoption of digital technologies to manage and process information used in everyday life, results in an increase in the demand for digital data analysis for investigative purposes. In fact, the reconstruction of computer and telematic crimes, or, in general, of crimes committed with computer systems, require the adoption of Computer Forensics best practices in order to extract relevant evidences from electronic devices, guaranteeing the integrity of data and their admissibility during a trial. The process of extraction, conservation, analysis and documentation of a forensic investigation can be enhanced by a framework that support investigators during their work, correlating evidences collected by different forensic tools. So, in this work we propose a semantic methodology and a system architecture for evidences correlation aiming to provide enhanced retrieval and reasoning capabilities.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/748588
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