Abstract
Threats to enterprises have become widespread in the last decade. A major source of such threats originates from insiders who have legitimate access to the organization's internal systems and databases. Therefore, preventing or responding to such incidents has become a challenging task. Digital forensics has grown into a de-facto standard in the examination of electronic evidence; however, a key barrier is often being able to associate an individual to the stolen data. Stolen credentials and the Trojan defense are two commonly cited arguments used. This paper proposes a model that can more inextricably links the use of information (e.g. images, documents and emails) to the individual users who use and access them through the use of steganography and transparent biometrics. The initial experimental results of the proposed approach have shown that it is possible to correlate an individual's biometric feature vector with a digital object (images) and still successfully recover the sample even with significant file modification. In addition, a reconstruction of the feature vector from these unmodified images was possible by using those generated imprints with an accuracy of 100% in some scenarios.
Original language | English |
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Title of host publication | 2016 International Conference on Cyber Security and Protection of Digital Services, Cyber Security 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 30-44 |
ISBN (Electronic) | 978-1509007097 |
ISBN (Print) | 978-1509007103 |
DOIs | |
Publication status | Published - 11 Jul 2016 |
Event | International Conference on Cyber Security and Protection of Digital Services, Cyber Security 2016 - London, United Kingdom Duration: 13 Jun 2016 → 14 Jun 2016 |
Conference
Conference | International Conference on Cyber Security and Protection of Digital Services, Cyber Security 2016 |
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Country/Territory | United Kingdom |
City | London |
Period | 13/06/16 → 14/06/16 |
Keywords
- biometrics
- data leakage
- Digital forensics
- grille cipher
- guilty identification.