Pith. sign in

REVIEW 1 cited by

Current Challenges and Future Research Areas for Digital Forensic Investigation

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1604.03850 v1 pith:HCJCX2J3 submitted 2016-04-13 cs.CR cs.CY

classification cs.CRcs.CY
keywords digitalforensicnumberanalysisdevicesfuturechallengescurrent
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Given the ever-increasing prevalence of technology in modern life, there is a corresponding increase in the likelihood of digital devices being pertinent to a criminal investigation or civil litigation. As a direct consequence, the number of investigations requiring digital forensic expertise is resulting in huge digital evidence backlogs being encountered by law enforcement agencies throughout the world. It can be anticipated that the number of cases requiring digital forensic analysis will greatly increase in the future. It is also likely that each case will require the analysis of an increasing number of devices including computers, smartphones, tablets, cloud-based services, Internet of Things devices, wearables, etc. The variety of new digital evidence sources pose new and challenging problems for the digital investigator from an identification, acquisition, storage and analysis perspective. This paper explores the current challenges contributing to the backlog in digital forensics from a technical standpoint and outlines a number of future research topics that could greatly contribute to a more efficient digital forensic process.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A compression based framework for the detection of anomalies in heterogeneous data sources

    cs.CR 2019-08 conditional novelty 4.0 of 10

    A single NCD-plus-SVM pipeline reaches 0.77 to 0.95 accuracy on five text classification tasks, but the parameter-free claim is undercut by per-dataset tuning and training-set evaluation.

Pith tools