Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency
- URL: http://arxiv.org/abs/2402.19366v2
- Date: Tue, 11 Jun 2024 10:01:05 GMT
- Title: Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency
- Authors: Akila Wickramasekara, Frank Breitinger, Mark Scanlon,
- Abstract summary: The growing number of cases that require digital forensic analysis raises concerns about the ability of law enforcement to conduct investigations promptly.
This paper delves into the potential and effectiveness of integrating Large Language Models into digital forensic investigation to address these challenges.
- Score: 0.1433758865948252
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The growing number of cases that require digital forensic analysis raises concerns about the ability of law enforcement to conduct investigations promptly. Consequently, this paper delves into the potential and effectiveness of integrating Large Language Models (LLMs) into digital forensic investigation to address these challenges. A comprehensive literature review is carried out, encompassing existing digital forensic models, tools, LLMs, deep learning techniques, and the use of LLMs in investigations. The review identifies current challenges within existing digital forensic processes and explores both the obstacles and possibilities of incorporating LLMs. In conclusion, the study asserts that the adoption of LLMs in digital forensics, with appropriate constraints, has the potential to improve investigation efficiency, improve traceability, and alleviate technical and judicial barriers faced by law enforcement entities.
Related papers
- Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents [67.07177243654485]
This survey collects and analyzes the different threats faced by large language models-based agents.
We identify six key features of LLM-based agents, based on which we summarize the current research progress.
We select four representative agents as case studies to analyze the risks they may face in practical use.
arXiv Detail & Related papers (2024-11-14T15:40:04Z) - A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law.
We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies.
We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
arXiv Detail & Related papers (2024-05-02T22:43:02Z) - Apprentices to Research Assistants: Advancing Research with Large Language Models [0.0]
Large Language Models (LLMs) have emerged as powerful tools in various research domains.
This article examines their potential through a literature review and firsthand experimentation.
arXiv Detail & Related papers (2024-04-09T15:53:06Z) - Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead [12.324949480085424]
There is currently no existing survey that focuses on the utilization of Large Language Models for vulnerability detection and repair.
This paper offers a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs.
arXiv Detail & Related papers (2024-04-03T07:27:33Z) - Enhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models [7.299483088092052]
This research focuses on maximizing the potential of prompting by placing it as the final phase of the retrieval system.
Experiments on the COLIEE 2023 dataset demonstrate that integrating prompting techniques on LLMs into the retrieval system significantly improves retrieval accuracy.
However, error analysis reveals several existing issues in the retrieval system that still need resolution.
arXiv Detail & Related papers (2024-03-26T20:25:53Z) - Large Language Models for Forecasting and Anomaly Detection: A
Systematic Literature Review [10.325003320290547]
This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection.
LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains.
This review identifies several critical challenges that impede their broader adoption and effectiveness, including the reliance on vast historical datasets, issues with generalizability across different contexts, and the phenomenon of model hallucinations.
arXiv Detail & Related papers (2024-02-15T22:43:02Z) - The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains.
This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
arXiv Detail & Related papers (2023-12-01T16:00:25Z) - A Survey on Detection of LLMs-Generated Content [97.87912800179531]
The ability to detect LLMs-generated content has become of paramount importance.
We aim to provide a detailed overview of existing detection strategies and benchmarks.
We also posit the necessity for a multi-faceted approach to defend against various attacks.
arXiv Detail & Related papers (2023-10-24T09:10:26Z) - A Comprehensive Analysis of the Role of Artificial Intelligence and
Machine Learning in Modern Digital Forensics and Incident Response [0.0]
The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response.
This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice.
Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats.
arXiv Detail & Related papers (2023-09-13T16:23:53Z) - A Comprehensive Overview of Large Language Models [68.22178313875618]
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks.
This article provides an overview of the existing literature on a broad range of LLM-related concepts.
arXiv Detail & Related papers (2023-07-12T20:01:52Z) - Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks.
We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
arXiv Detail & Related papers (2023-05-24T10:45:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.