[Paper Review] Current Challenges and Future Research Areas for Digital Forensic Investigation

David Lillis, Brett A. Becker+2 more|arXiv (Cornell University)|Jan 1, 2016

Digital and Cyber Forensics30 references63 citations

TL;DR

This paper identifies critical challenges in digital forensics—such as data volume, device heterogeneity, and evidence backlog—and proposes future research directions including Forensics-as-a-Service (FaaS), hardware-accelerated processing (FPGAs, GPUs), automated data deduplication, and advanced information retrieval techniques to improve analysis speed and efficiency. The key contribution is a roadmap for integrating high-performance computing and AI-driven methods to reduce manual work and expedite investigations.

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.

Motivation & Objective

• To analyze current technical challenges in digital forensics that contribute to evidence backlogs.
• To identify key research gaps in handling increasing data volumes, device diversity, and cloud/IoT evidence.
• To propose future research directions that enhance automation, scalability, and efficiency in digital forensic investigations.
• To reduce reliance on manual analysis by integrating advanced computing and data processing techniques.
• To improve timeliness and accuracy in digital evidence processing through standardized, scalable, and intelligent forensic systems.

Proposed method

• Proposes Forensics-as-a-Service (FaaS) to enable scalable, on-demand digital forensic processing via cloud infrastructure.
• Advocates for hardware acceleration using FPGAs and GPUs to speed up data acquisition, analysis, and indexing.
• Introduces data deduplication techniques to eliminate redundant processing of identical or similar evidence across cases.
• Applies Information Retrieval (IR) methods with configurable recall/precision trade-offs to prioritize relevant evidence during triage.
• Utilizes temporal data extraction from unstructured text to automate timeline reconstruction across multiple devices.
• Integrates parallel and distributed computing models to handle high-volume, heterogeneous digital evidence sources.

Experimental results

Research questions

• RQ1 How can Forensics-as-a-Service (FaaS) improve scalability and reduce processing delays in digital forensic investigations?
• RQ2 What role can FPGAs and GPU acceleration play in reducing the time required for data acquisition and analysis?
• RQ3 How can data deduplication techniques minimize redundant analysis of identical or similar digital evidence?
• RQ4 To what extent can configurable Information Retrieval (IR) systems enhance evidence triage with balanced recall and precision?
• RQ5 How can automated timeline reconstruction from heterogeneous data sources improve investigative correlation and consistency?

Key findings

• The FBI’s digital evidence volume increased 6.65-fold from 2003 to 2011, rising from 84GB to 559GB per case, highlighting the scale of the backlog problem.
• Cloud and IoT devices contribute significantly to data heterogeneity and complexity, complicating acquisition and analysis.
• FPGAs and GPUs offer high-speed processing for I/O-bound and compute-intensive forensic tasks, especially as SSDs reduce I/O bottlenecks.
• Information Retrieval techniques can accelerate triage by prioritizing relevant evidence, though recall-precision trade-offs require careful configuration.
• Data deduplication reduces redundant processing and storage, particularly for system files and common metadata.
• Automated timeline reconstruction using temporal extraction from unstructured text can reduce manual effort in correlating events across devices.