Cozzolino, Giovanni (2018) A semantic methodology for (un)structured digital evidences analysis. [Tesi di dottorato]
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Item Type: | Tesi di dottorato |
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Resource language: | English |
Title: | A semantic methodology for (un)structured digital evidences analysis |
Creators: | Creators Email Cozzolino, Giovanni giovanni.cozzolino@unina.it |
Date: | November 2018 |
Number of Pages: | 144 |
Institution: | Università degli Studi di Napoli Federico II |
Department: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
Dottorato: | Information technology and electrical engineering |
Ciclo di dottorato: | 31 |
Coordinatore del Corso di dottorato: | nome email Riccio, Daniele daniele.riccio@unina.it |
Tutor: | nome email Mazzeo, Antonino UNSPECIFIED Amato, Flora UNSPECIFIED |
Date: | November 2018 |
Number of Pages: | 144 |
Keywords: | semantic computer digital forensic correlation |
Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni |
Date Deposited: | 22 Jan 2019 22:15 |
Last Modified: | 30 Jun 2020 09:07 |
URI: | http://www.fedoa.unina.it/id/eprint/12687 |
Collection description
Nowadays, more than ever, digital forensics activities are involved in any criminal, civil or military investigation and represent a fundamental tool to support cyber-security. Investigators use a variety of techniques and proprietary software forensic applications to examine the copy of digital devices, searching hidden, deleted, encrypted, or damaged files or folders. Any evidence found is carefully analysed and documented in a "finding report" in preparation for legal proceedings that involve discovery, depositions, or actual litigation. The aim is to discover and analyse patterns of fraudulent activities. In this work, a new methodology is proposed to support investigators during the analysis process, correlating evidences found through different forensic tools. The methodology was implemented through a system able to add semantic assertion to data generated by forensics tools during extraction processes. These assertions enable more effective access to relevant information and enhanced retrieval and reasoning capabilities.
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