Document Type

Book Chapter

Publisher

Institute of Electrical and Electronics Engineers Inc

Place of Publication

United States

Faculty

Faculty of Business and Law

School

School of Engineering

RAS ID

22790

Funders

Australian Research Council,

Edith Cowan University Faculty of Business and Law Strategic Research Fund

National Science Council, Taiwan

Comments

This is an Author's Accepted Manuscript of:

Alruban, A., Clarke, N., Li, F., & Furnell, S. (2016). Proactive biometric-enabled forensic imprinting. 2016 International Conference On Cyber Security And Protection Of Digital Services (Cyber Security). https://doi.org/10.1109/CyberSecPODS.2016.7502342

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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.

DOI

10.1109/CyberSecPODS.2016.7502342

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