Mass image classification

Sanderson, Paul

doi:10.1016/j.diin.2006.10.010

Cited by 4 publications

(1 citation statement)

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“…Research focused on improving the efficiency and effectiveness of digital forensic analyses include distributed analytical processing [56], datamining-based search processes [5], file classification to aid analysis [60], self-organizing neural networks for thematically clustering string search results [6], and massive threading via graphical processing units (GPUs) [38]. Researchers have also investigated network-based architectures and virtualization infrastructures to facilitate large evidence stores, case and digital asset management systems, and collaborative, geographically distributed analysis [19,20].…”

Section: Volume and Scalabilitymentioning

confidence: 99%

Digital Forensic Research: The Good, the Bad and the Unaddressed

Beebe¹

2009

IFIP Advances in Information and Communication Technology

117

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Digital forensics is a relatively new scientific discipline, but one that has matured greatly over the past decade. In any field of human endeavor, it is important to periodically pause and review the state of the discipline. This paper examines where the discipline of digital forensics is at this It is a compilation of the author's opinion and the viewpoints of twenty-one other practitioners and researchers, many of whom are leaders in the field. In synthesizing these professional opinions, several consensus views emerge that provide valuable insights into the "state of the discipline."

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Section: Volume and Scalabilitymentioning

confidence: 99%

Digital Forensic Research: The Good, the Bad and the Unaddressed

Beebe¹

2009

IFIP Advances in Information and Communication Technology

117

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An Efficient Computer Forensics Selective Imaging Model

Halboob

Alghathbar

Mahmod

et al. 2014

Lecture Notes in Electrical Engineering

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Selective imaging is a new concept in computer forensics. It is used for collecting only the data that is relevant to the crime and helps in improves the scalability of the investigation process. However, the current selective imaging approaches directly image the identified data without considering their offsets on the targeted user storage. This paper investigates the impact of the relevant data offsets on the efficiency of the selective imaging process. A practical selective imaging model is presented which includes a digital evidence ordering algorithm (DEOA) for ordering the selected relevant data items. The proposed selective imaging model has been implemented and evaluated in different types of storage devices. The evaluation result shows that even if our proposed algorithm has a small efficiency negative impact before the imaging process starts; it has a large positive effect on the efficiency of the selective imaging process itself.

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