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.
Current Challenges and Future Research Areas for Digital Forensic Investigation
1 Pith paper cite this work. Polarity classification is still indexing.
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.
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cs.CR 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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A compression based framework for the detection of anomalies in heterogeneous data sources
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.